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Статті в журналах з теми "FEATURE OPTIMIZATION METHODS"
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Hamad, Zana O. "REVIEW OF FEATURE SELECTION METHODS USING OPTIMIZATION ALGORITHM." Polytechnic Journal 12, no. 2 (March 15, 2023): 203–14. http://dx.doi.org/10.25156/ptj.v12n2y2022.pp203-214.
Повний текст джерелаАнотація:
Many works have been done to reduce complexity in terms of time and memory space. The feature selection process is one of the strategies to reduce system complexity and can be defined as a process of selecting the most important feature among feature space. Therefore, the most useful features will be kept, and the less useful features will be eliminated. In the fault classification and diagnosis field, feature selection plays an important role in reducing dimensionality and sometimes might lead to having a high classification rate. In this paper, a comprehensive review is presented about feature selection processing and how it can be done. The primary goal of this research is to examine all of the strategies that have been used to highlight the (selection) selected process, including filter, wrapper, Meta-heuristic algorithm, and embedded. Review of Nature-inspired algorithms that have been used for features selection is more focused such as particle swarm, Grey Wolf, Bat, Genetic, wale, and ant colony algorithm. The overall results confirmed that the feature selection approach is important in reducing the complexity of any model-based machine learning algorithm and may sometimes result in improved performance of the simulated model.
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Zhang, Yang, Emil Tochev, Svetan Ratchev, and Carl German. "Production process optimization using feature selection methods." Procedia CIRP 88 (2020): 554–59. http://dx.doi.org/10.1016/j.procir.2020.05.096.
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Goodarzi, Mohammad, Bieke Dejaegher, and Yvan Vander Heyden. "Feature Selection Methods in QSAR Studies." Journal of AOAC INTERNATIONAL 95, no. 3 (May 1, 2012): 636–51. http://dx.doi.org/10.5740/jaoacint.sge_goodarzi.
Повний текст джерелаАнотація:
Abstract A quantitative structure-activity relationship (QSAR) relates quantitative chemical structure attributes (molecular descriptors) to a biological activity. QSAR studies have now become attractive in drug discovery and development because their application can save substantial time and human resources. Several parameters are important in the prediction ability of a QSAR model. On the one hand, different statistical methods may be applied to check the linear or nonlinear behavior of a data set. On the other hand, feature selection techniques are applied to decrease the model complexity, to decrease the overfitting/overtraining risk, and to select the most important descriptors from the often more than 1000 calculated. The selected descriptors are then linked to a biological activity of the corresponding compound by means of a mathematical model. Different modeling techniques can be applied, some of which explicitly require a feature selection. A QSAR model can be useful in the design of new compounds with improved potency in the class under study. Only molecules with a predicted interesting activity will be synthesized. In the feature selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus attention, while ignoring the rest. Up to now, many feature selection techniques, such as genetic algorithms, forward selection, backward elimination, stepwise regression, and simulated annealing have been used extensively. Swarm intelligence optimizations, such as ant colony optimization and partial swarm optimization, which are feature selection techniques usually simulated based on animal and insect life behavior to find the shortest path between a food source and their nests, recently are also involved in QSAR studies. This review paper provides an overview of different feature selection techniques applied in QSAR modeling.
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Jameel, Noor, and Hasanen S. Abdullah. "Intelligent Feature Selection Methods: A Survey." Engineering and Technology Journal 39, no. 1B (March 25, 2021): 175–83. http://dx.doi.org/10.30684/etj.v39i1b.1623.
Повний текст джерелаАнотація:
Consider feature selection is the main in intelligent algorithms and machine learning to select the subset of data to help acquire the optimal solution. Feature selection used an extract the relevance of the data and discarding the irrelevance of the data with increment fast to select it and to reduce the dimensional of dataset. In the past, it used traditional methods, but these methods are slow of fast and accuracy. In modern times, however, it uses the intelligent methods, Genetic algorithm and swarm optimization methods Ant colony, Bees colony, Cuckoo search, Particle optimization, fish algorithm, cat algorithm, Genetic algorithm ...etc. In feature selection because to increment fast, high accuracy and easy to use of user. In this paper survey it used the Some the swarm intelligent method: Ant colony, Bees colony, Cuckoo search, Particle optimization and Genetic algorithm (GA). It done take the related work for each algorithms the swarm intelligent the ideas, dataset and accuracy of the results after that was done to compare the result in the table among the algorithms and learning the better algorithm is discuses in the discussion and why it is better. Finally, it learning who is the advantage and disadvantage for each algorithms of swarm intelligent in feature selection.
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Wu, Shaohua, Yong Hu, Wei Wang, Xinyong Feng, and Wanneng Shu. "Application of Global Optimization Methods for Feature Selection and Machine Learning." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/241517.
Повний текст джерелаАнотація:
The feature selection process constitutes a commonly encountered problem of global combinatorial optimization. The process reduces the number of features by removing irrelevant and redundant data. This paper proposed a novel immune clonal genetic algorithm based on immune clonal algorithm designed to solve the feature selection problem. The proposed algorithm has more exploration and exploitation abilities due to the clonal selection theory, and each antibody in the search space specifies a subset of the possible features. Experimental results show that the proposed algorithm simplifies the feature selection process effectively and obtains higher classification accuracy than other feature selection algorithms.
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Wein, Fabian, Peter D. Dunning, and Julián A. Norato. "A review on feature-mapping methods for structural optimization." Structural and Multidisciplinary Optimization 62, no. 4 (August 3, 2020): 1597–638. http://dx.doi.org/10.1007/s00158-020-02649-6.
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Larabi-Marie-Sainte, Souad. "Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms." Applied Sciences 11, no. 15 (July 23, 2021): 6769. http://dx.doi.org/10.3390/app11156769.
Повний текст джерелаАнотація:
The curse of dimensionality problem occurs when the data are high-dimensional. It affects the learning process and reduces the accuracy. Feature selection is one of the dimensionality reduction approaches that mainly contribute to solving the curse of the dimensionality problem by selecting the relevant features. Irrelevant features are the dependent and redundant features that cause noise in the data and then reduce its quality. The main well-known feature-selection methods are wrapper and filter techniques. However, wrapper feature selection techniques are computationally expensive, whereas filter feature selection methods suffer from multicollinearity. In this research study, four new feature selection methods based on outlier detection using the Projection Pursuit method are proposed. Outlier detection involves identifying abnormal data (irrelevant features of the transpose matrix obtained from the original dataset matrix). The concept of outlier detection using projection pursuit has proved its efficiency in many applications but has not yet been used as a feature selection approach. To the author’s knowledge, this study is the first of its kind. Experimental results on nineteen real datasets using three classifiers (k-NN, SVM, and Random Forest) indicated that the suggested methods enhanced the classification accuracy rate by an average of 6.64% when compared to the classification accuracy without applying feature selection. It also outperformed the state-of-the-art methods on most of the used datasets with an improvement rate ranging between 0.76% and 30.64%. Statistical analysis showed that the results of the proposed methods are statistically significant.
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Boubezoul, Abderrahmane, and Sébastien Paris. "Application of global optimization methods to model and feature selection." Pattern Recognition 45, no. 10 (October 2012): 3676–86. http://dx.doi.org/10.1016/j.patcog.2012.04.015.
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Uzun, Mehmet Zahit, Yuksel Celik, and Erdal Basaran. "Micro-Expression Recognition by Using CNN Features with PSO Algorithm and SVM Methods." Traitement du Signal 39, no. 5 (November 30, 2022): 1685–93. http://dx.doi.org/10.18280/ts.390526.
Повний текст джерелаАнотація:
This study proposes a framework for defining ME expressions, in which preprocessing, feature extraction with deep learning, feature selection with an optimization algorithm, and classification methods are used. CASME-II, SMIC-HS, and SAMM, which are among the most used ME datasets in the literature, were combined to overcome the under-sampling problem caused by the datasets. In the preprocessing stage, onset, and apex frames in each video clip in datasets were detected, and optical flow images were obtained from the frames using the FarneBack method. The features of these obtained images were extracted by applying AlexNet, VGG16, MobilenetV2, EfficientNet, Squeezenet from CNN models. Then, combining the image features obtained from all CNN models. And then, the ones which are the most distinctive features were selected with the Particle Swarm Optimization (PSO) algorithm. The new feature set obtained was divided into classes positive, negative, and surprise using SVM. As a result, its success has been demonstrated with an accuracy rate of 0.8784 obtained in our proposed ME framework.
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Liu, Yong Xia, Ru Shu Peng, Ai Hong Hou, and De Wen Tang. "Methods of Cam Structure Optimization Based on Behavioral Modeling." Advanced Materials Research 139-141 (October 2010): 1245–48. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.1245.
Повний текст джерелаАнотація:
By defining analysis feature and using analysis results to drive the parametric model, behavioral modeling establishes feature parameter of model automatically for meeting design objectives and making model technology intelligent, that is to say the result could be optimized automatically. In this paper, the application of PRO/E parametric modeling technologies in design of cam profile curve is researched. In order to optimization for dynamic balance of the cam, the methods of defining analysis feature and sensitivity/optimization analysis are proposed by using the technology of PRO/E behavioral modeling. These technologies can enhance the efficiency and quality of the cam design and provide a practical method for 3D modeling of this kind of cam. The fifth generation of CAD model technology named behavioral modeling provides the method of flexible and intelligent solution of practical engineering problems.
Дисертації з теми "FEATURE OPTIMIZATION METHODS"
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Lin, Lei. "Optimization methods for inventive design." Thesis, Strasbourg, 2016. http://www.theses.fr/2016STRAD012/document.
Повний текст джерелаАнотація:
La thèse traite des problèmes d'invention où les solutions des méthodes d'optimisation ne satisfont pas aux objectifs des problèmes à résoudre. Les problèmes ainsi définis exploitent, pour leur résolution, un modèle de problème étendant le modèle de la TRIZ classique sous une forme canonique appelée "système de contradictions généralisées". Cette recherche instrumente un processus de résolution basé sur la boucle simulation-optimisation-invention permettant d'utiliser à la fois des méthodes d'optimisation et d'invention. Plus précisément, elle modélise l'extraction des contractions généralisées à partir des données de simulation sous forme de problèmes d'optimisation combinatoire et propose des algorithmes donnant toutes les solutions à ces problèmesThe thesis deals with problems of invention where solutions optimization methods do not meet the objectives of problems to solve. The problems previuosly defined exploit for their resolution, a problem extending the model of classical TRIZ in a canonical form called "generalized system of contradictions." This research draws up a resolution process based on the loop simulation-optimization-invention using both solving methods of optimization and invention. More precisely, it models the extraction of generalized contractions from simulation data as combinatorial optimization problems and offers algorithms that offer all the solutions to these problems
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Zanco, Philip. "Analysis of Optimization Methods in Multisteerable Filter Design." ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/td/2227.
Повний текст джерелаАнотація:
The purpose of this thesis is to study and investigate a practical and efficient implementation of corner orientation detection using multisteerable filters. First, practical theory involved in applying multisteerable filters for corner orientation estimation is presented. Methods to improve the efficiency with which multisteerable corner filters are applied to images are investigated and presented. Prior research in this area presented an optimization equation for determining the best match of corner orientations in images; however, little research has been done on optimization techniques to solve this equation. Optimization techniques to find the maximum response of a similarity function to determine how similar a corner feature is to a multioriented corner template are also explored and compared in this research.
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Monrousseau, Thomas. "Développement du système d'analyse des données recueillies par les capteurs et choix du groupement de capteurs optimal pour le suivi de la cuisson des aliments dans un four." Thesis, Toulouse, INSA, 2016. http://www.theses.fr/2016ISAT0054.
Повний текст джерелаАнотація:
Dans un monde où tous les appareils électro-ménagers se connectent et deviennent intelligents, il est apparu pour des industriels français le besoin de créer des fours de cuisson innovants capables de suivre l’état de cuisson à cœur de poissons et de viandes sans capteur au contact. Cette thèse se place dans ce contexte et se divise en deux grandes parties. La première est une phase de sélection d’attributs parmi un ensemble de mesures issues de capteurs spécifiques de laboratoire afin de permettre d’appliquer un algorithme de classification supervisée sur trois états de cuisson. Une méthode de sélection basée sur la logique floue a notamment été appliquée pour réduire grandement le nombre de variable à surveiller. La seconde partie concerne la phase de suivi de cuisson en ligne par plusieurs méthodes. Les techniques employées sont une approche par classification sur dix états à cœur, la résolution d’équation de la chaleur discrétisée, ainsi que le développement d’un capteur logiciel basé sur des réseaux de neurones artificiels synthétisés à partir d’expériences de cuisson, pour réaliser la reconstruction du signal de la température au cœur des aliments à partir de mesures disponibles en ligne. Ces algorithmes ont été implantés sur microcontrôleur équipant une version prototype d’un nouveau four afin d’être testés et validés dans le cas d’utilisations réellesIn a world where all personal devices become smart and connected, some French industrials created a project to make ovens able detecting the cooking state of fish and meat without contact sensor. This thesis takes place in this context and is divided in two major parts. The first one is a feature selection phase to be able to classify food in three states: under baked, well baked and over baked. The point of this selection method, based on fuzzy logic is to strongly reduce the number of features got from laboratory specific sensors. The second part concerns on-line monitoring of the food cooking state by several methods. These technics are: classification algorithm into ten bake states, the use of a discrete version of the heat equation and the development of a soft sensor based on an artificial neural network model build from cooking experiments to infer the temperature inside the food from available on-line measurements. These algorithms have been implemented on microcontroller equipping a prototype version of a new oven in order to be tested and validated on real use cases
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Xiong, Xuehan. "Supervised Descent Method." Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/652.
Повний текст джерелаАнотація:
In this dissertation, we focus on solving Nonlinear Least Squares problems using a supervised approach. In particular, we developed a Supervised Descent Method (SDM), performed thorough theoretical analysis, and demonstrated its effectiveness on optimizing analytic functions, and four other real-world applications: Inverse Kinematics, Rigid Tracking, Face Alignment (frontal and multi-view), and 3D Object Pose Estimation. In Rigid Tracking, SDM was able to take advantage of more robust features, such as, HoG and SIFT. Those non-differentiable image features were out of consideration of previous work because they relied on gradient-based methods for optimization. In Inverse Kinematics where we minimize a non-convex function, SDM achieved significantly better convergence than gradient-based approaches. In Face Alignment, SDM achieved state-of-the-arts results. Moreover, it was extremely computationally efficient, which makes it applicable for many mobile applications. In addition, we provided a unified view of several popular methods including SDM on sequential prediction, and reformulated them as a sequence of function compositions. Finally, we suggested some future research directions on SDM and sequential prediction.
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Lösch, Felix. "Optimization of variability in software product lines a semi-automatic method for visualization, analysis, and restructuring of variability in software product lines." Berlin Logos-Verl, 2008. http://d-nb.info/992075904/04.
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Bai, Bing. "A Study of Adaptive Random Features Models in Machine Learning based on Metropolis Sampling." Thesis, KTH, Numerisk analys, NA, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-293323.
Повний текст джерелаАнотація:
Artificial neural network (ANN) is a machine learning approach where parameters, i.e., frequency parameters and amplitude parameters, are learnt during the training process. Random features model is a special case of ANN that the structure of random features model is as same as ANN’s but the parameters’ learning processes are different. For random features model, the amplitude parameters are learnt during the training process but the frequency parameters are sampled from some distributions. If the frequency distribution of the random features model is well-chosen, both models can approximate data well. Adaptive random Fourier features with Metropolis sampling is an enhanced random Fourier features model which can select appropriate frequency distribution adaptively. This thesis studies Rectified Linear Unit and sigmoid features and combines them with the adaptive idea to generate another two adaptive random features models. The results show that using the particular set of hyper-parameters, adaptive random Rectified Linear Unit features model can also approximate the data relatively well, though the adaptive random Fourier features model performs slightly better.I artificiella neurala nätverk (ANN), som används inom maskininlärning, behöver parametrar, kallade frekvensparametrar och amplitudparametrar, hittasgenom en så kallad träningsprocess. Random feature-modeller är ett specialfall av ANN där träningen sker på ett annat sätt. I dessa modeller tränasamplitudparametrarna medan frekvensparametrarna samplas från någon sannolikhetstäthet. Om denna sannolikhetstäthet valts med omsorg kommer båda träningsmodellerna att ge god approximation av givna data. Metoden Adaptiv random Fourier feature[1] uppdaterar frekvensfördelningen adaptivt. Denna uppsats studerar aktiveringsfunktionerna ReLU och sigmoid och kombinerar dem med den adaptiva iden i [1] för att generera två ytterligare Random feature-modeller. Resultaten visar att om samma hyperparametrar som i [1] används så kan den adaptiva ReLU features-modellen approximera data relativt väl, även om Fourier features-modellen ger något bättre resultat.
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Sasse, Hugh Granville. "Enhancing numerical modelling efficiency for electromagnetic simulation of physical layer components." Thesis, De Montfort University, 2010. http://hdl.handle.net/2086/4406.
Повний текст джерелаАнотація:
The purpose of this thesis is to present solutions to overcome several key difficulties that limit the application of numerical modelling in communication cable design and analysis. In particular, specific limiting factors are that simulations are time consuming, and the process of comparison requires skill and is poorly defined and understood. When much of the process of design consists of optimisation of performance within a well defined domain, the use of artificial intelligence techniques may reduce or remove the need for human interaction in the design process. The automation of human processes allows round-the-clock operation at a faster throughput. Achieving a speedup would permit greater exploration of the possible designs, improving understanding of the domain. This thesis presents work that relates to three facets of the efficiency of numerical modelling: minimizing simulation execution time, controlling optimization processes and quantifying comparisons of results. These topics are of interest because simulation times for most problems of interest run into tens of hours. The design process for most systems being modelled may be considered an optimisation process in so far as the design is improved based upon a comparison of the test results with a specification. Development of software to automate this process permits the improvements to continue outside working hours, and produces decisions unaffected by the psychological state of a human operator. Improved performance of simulation tools would facilitate exploration of more variations on a design, which would improve understanding of the problem domain, promoting a virtuous circle of design. The minimization of execution time was achieved through the development of a Parallel TLM Solver which did not use specialized hardware or a dedicated network. Its design was novel because it was intended to operate on a network of heterogeneous machines in a manner which was fault tolerant, and included a means to reduce vulnerability of simulated data without encryption. Optimisation processes were controlled by genetic algorithms and particle swarm optimisation which were novel applications in communication cable design. The work extended the range of cable parameters, reducing conductor diameters for twisted pair cables, and reducing optical coverage of screens for a given shielding effectiveness. Work on the comparison of results introduced ―Colour maps‖ as a way of displaying three scalar variables over a two-dimensional surface, and comparisons were quantified by extending 1D Feature Selective Validation (FSV) to two dimensions, using an ellipse shaped filter, in such a way that it could be extended to higher dimensions. In so doing, some problems with FSV were detected, and suggestions for overcoming these presented: such as the special case of zero valued DC signals. A re-description of Feature Selective Validation, using Jacobians and tensors is proposed, in order to facilitate its implementation in higher dimensional spaces.
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YADAV, JYOTI. "A STUDY OF FEATURE OPTIMIZATION METHODS FOR LUNG CANCER DETECTION." Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19156.
Повний текст джерелаАнотація:
In this project, Lung cancer remains an extremely important disease in the world that causes deaths.
Early Diagnosis can prevent large amounts of deaths. Classifiers play an important role in detecting
lung cancer by means of a machine learning set of rules in addition to CAD-based image processing
techniques. For the classifier’s accuracy, there is the need for a good feature collection of images.
Features of an image can help to find all relevant information for identifying disease. Features are the
important parameter for finding results. Mostly, features are extracted from feature extraction
techniques like GLCM or some datasets already have features of lung cancer images by using some
techniques. For different models of classifier, dimension, storage, speed, time and performance create
an impactful effect on the results because we have large amount features of the images. An optimized
method like the feature selection technique is the one solution that leads to finding relevant features
from datasets containing features or features extracted from feature extraction techniques. The lung
cancer database has 32 case records with 57 unique characteristics. Hong and Young compiled this
database, which was indexed in the University of California Irvine repository. Take out medical
information and X-ray information, for example, are among the experimental materials. The data
described three categories of problematic lung malignancies, each with an integer value ranging from
0 to 3. A new strategy for identifying effective aspects of lung cancer is proposed in our work in
Matlab 2022a. It employs a Genetic Algorithm. Using a simplified 8-feature SVM classifier and four feature KNN, 100% accurateness is achieved. The new method is compared to the existing Hyper Heuristic method for the feature selection. Through the maximum level of precision, the projected
technique performs better. As a result, the proposed approach is recommended for determining an
effective disease symptom.
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Salehipour, Amir. "Combinatorial optimization methods for the (alpha,beta)-k Feature Set Problem." Thesis, 2019. http://hdl.handle.net/1959.13/1400399.
Повний текст джерелаАнотація:
Research Doctorate - Doctor of Philosophy (PhD)This PhD research thesis proposes novel and efficient combinatorial optimization-based solution methods for the (alpha,beta)-k Feature Set Problem. The (alpha,beta)-k Feature Set Problem is a combinatorial optimization-based feature selection approach proposed in 2004, and has several applications in computational biology and Bioinformatics. The (alpha,beta)-k Feature Set Problem aims to select a minimum cost set of features such that similarities between entities of the same class and differences between entities of different classes are maximized. The developed solution methods of this research include heuristic and exact methods. While this research focuses on utilizing exact methods, we also developed mathematical properties, and heuristics and problem-driven local searches and applied them in certain stages of the exact methods in order to guide exact solvers and deliver high quality solutions. The motivation behind this stems from computational difficulty of exact solvers in providing good quality solutions for the (alpha, beta)-k Feature Set Problem. Our proposed heuristics deliver very good quality solutions including optimal, and that in a reasonable amount of time. The major contributions of the presented research include: 1) investigating and exploring mathematical properties and characteristics of the (alpha,beta)-k Feature Set Problem for the first time, and utilizing those in order to design and develop algorithms and methods for solving large instances of the (alpha,beta)-k Feature Set Problem; 2) extending the basic modeling, algorithms and solution methods to the weighted variant of the (alpha,beta)-k Feature Set Problem (where features have a cost); and, 3) developing algorithms and solution methods that are capable of solving large instances of the (alpha,beta)-k Feature Set Problem in a reasonable amount of time (prior to this research, many of those instances pose a computational challenge for the exact solvers). To this end, we showed the usefulness of the developed algorithms and methods by applying them on three sets of 346 instances, including real-world, weighted, and randomly generated instances, and obtaining high quality solutions in a short time. To the best of our knowledge, the developed algorithms of this research have obtained the best results for the (alpha,beta)-k Feature Set Problem. In particular, they outperform state-of-the-art algorithms and exact solvers, and have a very competitive performance over large instances because they always deliver feasible solutions, and obtain new best solutions for a majority of large instances in a reasonable amount of time.
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Tayal, Aditya. "Effective and Efficient Optimization Methods for Kernel Based Classification Problems." Thesis, 2014. http://hdl.handle.net/10012/8334.
Повний текст джерелаАнотація:
Kernel methods are a popular choice in solving a number of problems in statistical machine learning. In this thesis, we propose new methods for two important kernel based classification problems: 1) learning from highly unbalanced large-scale datasets and 2) selecting a relevant subset of input features for a given kernel specification.
The first problem is known as the rare class problem, which is characterized by a highly skewed or unbalanced class distribution. Unbalanced datasets can introduce significant bias in standard classification methods. In addition, due to the increase of data in recent years, large datasets with millions of observations have become commonplace. We propose an approach to address both the problem of bias and computational complexity in rare class problems by optimizing area under the receiver operating characteristic curve and by using a rare class only kernel representation, respectively. We justify the proposed approach theoretically and computationally. Theoretically, we establish an upper bound on the difference between selecting a hypothesis from a reproducing kernel Hilbert space and a hypothesis space which can be represented using a subset of kernel functions. This bound shows that for a fixed number of kernel functions, it is optimal to first include functions corresponding to rare class samples. We also discuss the connection of a subset kernel representation with the Nystrom method for a general class of regularized loss minimization methods. Computationally, we illustrate that the rare class representation produces statistically equivalent test error results on highly unbalanced datasets compared to using the full kernel representation, but with significantly better time and space complexity. Finally, we extend the method to rare class ordinal ranking, and apply it to a recent public competition problem in health informatics.
The second problem studied in the thesis is known as the feature selection problem in literature. Embedding feature selection in kernel classification leads to a non-convex optimization problem. We specify a primal formulation and solve the problem using a second-order trust region algorithm. To improve efficiency, we use the two-block Gauss-Seidel method, breaking the problem into a convex support vector machine subproblem and a non-convex feature selection subproblem. We reduce possibility of saddle point convergence and improve solution quality by sharing an explicit functional margin variable between block iterates. We illustrate how our algorithm improves upon state-of-the-art methods.
Книги з теми "FEATURE OPTIMIZATION METHODS"
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The Feature-Driven Method for Structural Optimization. Elsevier, 2021. http://dx.doi.org/10.1016/c2019-0-03253-0.
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Feature-Driven Method for Structural Optimization Design. Elsevier, 2020.
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Zhou, Ying, and Weihong Zhang. Feature-Driven Method for Structural Optimization Design. Elsevier, 2021.
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Hilgurt, S. Ya, and O. A. Chemerys. Reconfigurable signature-based information security tools of computer systems. PH “Akademperiodyka”, 2022. http://dx.doi.org/10.15407/akademperiodyka.458.297.
Повний текст джерелаАнотація:
The book is devoted to the research and development of methods for combining computational structures for reconfigurable signature-based information protection tools for computer systems and networks in order to increase their efficiency. Network security tools based, among others, on such AI-based approaches as deep neural networking, despite the great progress shown in recent years, still suffer from nonzero recognition error probability. Even a low probability of such an error in a critical infrastructure can be disastrous. Therefore, signature-based recognition methods with their theoretically exact matching feature are still relevant when creating information security systems such as network intrusion detection systems, antivirus, anti-spam, and wormcontainment systems. The real time multi-pattern string matching task has been a major performance bottleneck in such systems. To speed up the recognition process, developers use a reconfigurable hardware platform based on FPGA devices. Such platform provides almost software flexibility and near-ASIC performance. The most important component of a signature-based information security system in terms of efficiency is the recognition module, in which the multipattern matching task is directly solved. It must not only check each byte of input data at speeds of tens and hundreds of gigabits/sec against hundreds of thousand or even millions patterns of signature database, but also change its structure every time a new signature appears or the operating conditions of the protected system change. As a result of the analysis of numerous examples of the development of reconfigurable information security systems, three most promising approaches to the construction of hardware circuits of recognition modules were identified, namely, content-addressable memory based on digital comparators, Bloom filter and Aho–Corasick finite automata. A method for fast quantification of components of recognition module and the entire system was proposed. The method makes it possible to exclude resource-intensive procedures for synthesizing digital circuits on FPGAs when building complex reconfigurable information security systems and their components. To improve the efficiency of the systems under study, structural-level combinational methods are proposed, which allow combining into single recognition device several matching schemes built on different approaches and their modifications, in such a way that their advantages are enhanced and disadvantages are eliminated. In order to achieve the maximum efficiency of combining methods, optimization methods are used. The methods of: parallel combining, sequential cascading and vertical junction have been formulated and investigated. The principle of multi-level combining of combining methods is also considered and researched. Algorithms for the implementation of the proposed combining methods have been developed. Software has been created that allows to conduct experiments with the developed methods and tools. Quantitative estimates are obtained for increasing the efficiency of constructing recognition modules as a result of using combination methods. The issue of optimization of reconfigurable devices presented in hardware description languages is considered. A modification of the method of affine transformations, which allows parallelizing such cycles that cannot be optimized by other methods, was presented. In order to facilitate the practical application of the developed methods and tools, a web service using high-performance computer technologies of grid and cloud computing was considered. The proposed methods to increase efficiency of matching procedure can also be used to solve important problems in other fields of science as data mining, analysis of DNA molecules, etc. Keywords: information security, signature, multi-pattern matching, FPGA, structural combining, efficiency, optimization, hardware description language.
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Zaheer Ul-Haq and Angela K. Wilson, eds. Frontiers in Computational Chemistry: Volume 6. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150368481220601.
Повний текст джерелаАнотація:
Frontiers in Computational Chemistry presents contemporary research on molecular modeling techniques used in drug discovery and the drug development process: computer aided molecular design, drug discovery and development, lead generation, lead optimization, database management, computer and molecular graphics, and the development of new computational methods or efficient algorithms for the simulation of chemical phenomena including analyses of biological activity. The sixth volume of this series features these six different perspectives on the application of computational chemistry in rational drug design: 1. Computer-aided molecular design in computational chemistry 2. The role of ensemble conformational sampling using molecular docking & dynamics in drug discovery 3. Molecular dynamics applied to discover antiviral agents 4. Pharmacophore modeling approach in drug discovery against the tropical infectious disease malaria 5. Advances in computational network pharmacology for Traditional Chinese Medicine (TCM) research 6. Progress in electronic-structure based computational methods: from small molecules to large molecular systems of biological significance
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Bacior, Stanisław. Optymalizacja wiejskich układów gruntowych – badania eksperymentalne. Publishing House of the University of Agriculture in Krakow, 2019. http://dx.doi.org/10.15576/978-83-66602-37-3.
Повний текст джерелаАнотація:
Rural areas are subject to constant structural, spatial and economic transformations. The main purpose of this monograph was to present a new concept of shaping of rural land arrangement that takes into account the land value. The presented optimization methodology of shaping of the rural areas has a general range of application, not being limited by time or place. of the location of the consolidation object. The only condition for its use is the availability of a specific set of output data enabling the necessary calculations for the implementation of consolidation works. The described method has been successfully applied to the research object of the Mściowojów village, in a registry area located in the Dolnośląkie voivodeship, in the Jaworski district, providing with the assumed effects. In order to meet the research objectives, the shaping of rural land arrangement was conducted according to five models. The original arrangement of existing land division in a given village is considered as the 1st model. The 2nd model uses a rather accurate description of the locations of the lands in the village. To define this feature the location of farm parcels had to be determined. This model is the most accurate, but also the most labor-intensive of all. In the 3rd model, a fundamental simplification of the land arrangement was adopted, limiting the distance matrix to its measurement to the entry points from the settlements into the complexes. This simplification means that the location of parcels in the complex does not affect the average distance to the land in the whole village. On the basis of simplifications applied in the 3rd model allowing a significant reduction of the distance matrix the 4th model which uses a linear programming to minimize the distance to a parcel was developed. Introducing into the linear model an additional condition that eliminates distance growth in farms in relation to the initial state was important for the research. This was implemented in the 5th model and had a positive impact on the obtained results. The 6th model was developed by including the landowners' wants into the 5th model. These had to be taken into account so that the research/the new land arrangement did not cause complaints. The wants could not be fully included due to their inherently contradictory nature. The wants for having the parcel in a given arrangement was replaced with a guarantee of division, after which landowner receives no smaller share than the prior one. As demonstrated in the work, the solutions of the developed models allowed obtaining land arrangements close to the optimal in terms of distance to land and the shape of parcels and farms with regard to land specifics. The presented results allow to draw a conclusion that the methods and analyses applied in the research can have a wide range of application in shaping of rural land arrangement. Developing the most socially accepted optimization of parcel division in the process of land consolidation is important due to the actual needs for the implementation of the rural land arrangement research. This may also have influence on better use of the EU's financial resources for the consolidation of agricultural lands.
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Railsback, Steven F., and Bret C. Harvey. Modeling Populations of Adaptive Individuals. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691195285.001.0001.
Повний текст джерелаАнотація:
Ecologists now recognize that the dynamics of populations, communities, and ecosystems are strongly affected by adaptive individual behaviors. Yet until now, we have lacked effective and flexible methods for modeling such dynamics. Traditional ecological models become impractical with the inclusion of behavior, and the optimization approaches of behavioral ecology cannot be used when future conditions are unpredictable due to feedbacks from the behavior of other individuals. This book provides a comprehensive introduction to state- and prediction-based theory, or SPT, a powerful new approach to modeling trade-off behaviors in contexts such as individual-based population models where feedbacks and variability make optimization impossible. This book features a wealth of examples that range from highly simplified behavior models to complex population models in which individuals make adaptive trade-off decisions about habitat and activity selection in highly heterogeneous environments. The book explains how SPT builds on key concepts from the state-based dynamic modeling theory of behavioral ecology, and how it combines explicit predictions of future conditions with approximations of a fitness measure to represent how individuals make good—not optimal—decisions that they revise as conditions change. The resulting models are realistic, testable, adaptable, and invaluable for answering fundamental questions in ecology and forecasting ecological outcomes of real-world scenarios.
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Mehta, Vaishali, Dolly Sharma, Monika Mangla, Anita Gehlot, Rajesh Singh, and Sergio Márquez Sánchez, eds. Challenges and Opportunities for Deep Learning Applications in Industry 4.0. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150360601220101.
Повний текст джерелаАнотація:
The competence of deep learning for the automation and manufacturing sector has received astonishing attention in recent times. The manufacturing industry has recently experienced a revolutionary advancement despite several issues. One of the limitations for technical progress is the bottleneck encountered due to the enormous increase in data volume for processing, comprising various formats, semantics, qualities and features. Deep learning enables detection of meaningful features that are difficult to perform using traditional methods. The book takes the reader on a technological voyage of the industry 4.0 space. Chapters highlight recent applications of deep learning and the associated challenges and opportunities it presents for automating industrial processes and smart applications. Chapters introduce the reader to a broad range of topics in deep learning and machine learning. Several deep learning techniques used by industrial professionals are covered, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical project methodology. Readers will find information on the value of deep learning in applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. The book also discusses prospective research directions that focus on the theory and practical applications of deep learning in industrial automation. Therefore, the book aims to serve as a comprehensive reference guide for industrial consultants interested in industry 4.0, and as a handbook for beginners in data science and advanced computer science courses.
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Ufimtseva, Nataliya V., Iosif A. Sternin, and Elena Yu Myagkova. Russian psycholinguistics: results and prospects (1966–2021): a research monograph. Institute of Linguistics, Russian Academy of Sciences, 2021. http://dx.doi.org/10.30982/978-5-6045633-7-3.
Повний текст джерелаАнотація:
The monograph reflects the problems of Russian psycholinguistics from the moment of its inception in Russia to the present day and presents its main directions that are currently developing. In addition, theoretical developments and practical results obtained in the framework of different directions and research centers are described in a concise form. The task of the book is to reflect, as far as it is possible in one edition, firstly, the history of the formation of Russian psycholinguistics; secondly, its methodology and developed methods; thirdly, the results obtained in different research centers and directions in different regions of Russia; fourthly, to outline the main directions of the further development of Russian psycholinguistics. There is no doubt that in the theoretical, methodological and applied aspects, the main problems and the results of their development by Russian psycholinguistics have no analogues in world linguistics and psycholinguistics, or are represented by completely original concepts and methods. We have tried to show this uniqueness of the problematics and the methodological equipment of Russian psycholinguistics in this book. The main role in the formation of Russian psycholinguistics was played by the Moscow psycholinguistic school of A.A. Leontyev. It still defines the main directions of Russian psycholinguistics. Russian psycholinguistics (the theory of speech activity - TSA) is based on the achievements of Russian psychology: a cultural-historical approach to the analysis of mental phenomena L.S. Vygotsky and the system-activity approach of A.N. Leontyev. Moscow is the most "psycholinguistic region" of Russia - INL RAS, Moscow State University, Moscow State Linguistic University, RUDN, Moscow State Pedagogical University, Moscow State Pedagogical University, Sechenov University, Moscow State University and other Moscow universities. Saint Petersburg psycholinguists have significant achievements, especially in the study of neurolinguistic problems, ontolinguistics. The most important feature of Russian psycholinguistics is the widespread development of psycholinguistics in the regions, the emergence of recognized psycholinguistic research centers - St. Petersburg, Tver, Saratov, Perm, Ufa, Omsk, Novosibirsk, Voronezh, Yekaterinburg, Kursk, Chelyabinsk; psycholinguistics is represented in Cherepovets, Ivanovo, Volgograd, Vyatka, Kaluga, Krasnoyarsk, Irkutsk, Vladivostok, Abakan, Maikop, Barnaul, Ulan-Ude, Yakutsk, Syktyvkar, Armavir and other cities; in Belarus - Minsk, in Ukraine - Lvov, Chernivtsi, Kharkov, in the DPR - Donetsk, in Kazakhstan - Alma-Ata, Chimkent. Our researchers work in Bulgaria, Hungary, Vietnam, China, France, Switzerland. There are Russian psycholinguists in Canada, USA, Israel, Austria and a number of other countries. All scientists from these regions and countries have contributed to the development of Russian psycholinguistics, to the development of psycholinguistic theory and methods of psycholinguistic research. Their participation has not been forgotten. We tried to present the main Russian psycholinguists in the Appendix - in the sections "Scientometrics", "Monographs and Manuals" and "Dissertations", even if there is no information about them in the Electronic Library and RSCI. The principles of including scientists in the scientometric list are presented in the Appendix. Our analysis of the content of the resulting monograph on psycholinguistic research in Russia allows us to draw preliminary conclusions about some of the distinctive features of Russian psycholinguistics: 1. cultural-historical approach to the analysis of mental phenomena of L.S.Vygotsky and the system-activity approach of A.N. Leontiev as methodological basis of Russian psycholinguistics; 2. theoretical nature of psycholinguistic research as a characteristic feature of Russian psycholinguistics. Our psycholinguistics has always built a general theory of the generation and perception of speech, mental vocabulary, linked specific research with the problems of ontogenesis, the relationship between language and thinking; 3. psycholinguistic studies of speech communication as an important subject of psycholinguistics; 4. attention to the psycholinguistic analysis of the text and the development of methods for such analysis; 5. active research into the ontogenesis of linguistic ability; 6. investigation of linguistic consciousness as one of the important subjects of psycholinguistics; 7. understanding the need to create associative dictionaries of different types as the most important practical task of psycholinguistics; 8. widespread use of psycholinguistic methods for applied purposes, active development of applied psycholinguistics. The review of the main directions of development of Russian psycholinguistics, carried out in this monograph, clearly shows that the direction associated with the study of linguistic consciousness is currently being most intensively developed in modern Russian psycholinguistics. As the practice of many years of psycholinguistic research in our country shows, the subject of study of psycholinguists is precisely linguistic consciousness - this is a part of human consciousness that is responsible for generating, understanding speech and keeping language in consciousness. Associative experiments are the core of most psycholinguistic techniques and are important both theoretically and practically. The following main areas of practical application of the results of associative experiments can be outlined. 1. Education. Associative experiments are the basis for constructing Mind Maps, one of the most promising tools for systematizing knowledge, assessing the quality, volume and nature of declarative knowledge (and using special techniques and skills). Methods based on smart maps are already widely used in teaching foreign languages, fast and deep immersion in various subject areas. 2. Information search, search optimization. The results of associative experiments can significantly improve the quality of information retrieval, its efficiency, as well as adaptability for a specific person (social group). When promoting sites (promoting them in search results), an associative experiment allows you to increase and improve the quality of the audience reached. 3. Translation studies, translation automation. An associative experiment can significantly improve the quality of translation, take into account intercultural and other social characteristics of native speakers. 4. Computational linguistics and automatic word processing. The results of associative experiments make it possible to reveal the features of a person's linguistic consciousness and contribute to the development of automatic text processing systems in a wide range of applications of natural language interfaces of computer programs and robotic solutions. 5. Advertising. The use of data on associations for specific words, slogans and texts allows you to predict and improve advertising texts. 6. Social relationships. The analysis of texts using the data of associative experiments makes it possible to assess the tonality of messages (negative / positive moods, aggression and other characteristics) based on user comments on the Internet and social networks, in the press in various projections (by individuals, events, organizations, etc.) from various social angles, to diagnose the formation of extremist ideas. 7. Content control and protection of personal data. Associative experiments improve the quality of content detection and filtering by identifying associative fields in areas subject to age restrictions, personal information, tobacco and alcohol advertising, incitement to ethnic hatred, etc. 8. Gender and individual differences. The data of associative experiments can be used to compare the reactions (and, in general, other features of thinking) between men and women, different social and age groups, representatives of different regions. The directions for the further development of Russian psycholinguistics from the standpoint of the current state of psycholinguistic science in the country are seen by us, first of all: in the development of research in various areas of linguistic consciousness, which will contribute to the development of an important concept of speech as a verbal model of non-linguistic consciousness, in which knowledge revealed by social practice and assigned by each member of society during its inculturation is consolidated for society and on its behalf; in the expansion of the problematics, which is formed under the influence of the growing intercultural communication in the world community, which inevitably involves the speech behavior of natural and artificial bilinguals in the new object area of psycholinguistics; in using the capabilities of national linguistic corpora in the interests of researchers studying the functioning of non-linguistic and linguistic consciousness in speech processes; in expanding research on the semantic perception of multimodal texts, the scope of which has greatly expanded in connection with the spread of the Internet as a means of communication in the life of modern society; in the inclusion of the problems of professional communication and professional activity in the object area of psycholinguistics in connection with the introduction of information technologies into public practice, entailing the emergence of new professions and new features of the professional ethos; in the further development of the theory of the mental lexicon (identifying the role of different types of knowledge in its formation and functioning, the role of the word as a unit of the mental lexicon in the formation of the image of the world, as well as the role of the natural / internal metalanguage and its specificity in speech activity); in the broad development of associative lexicography, which will meet the most diverse needs of society and cognitive sciences. The development of associative lexicography may lead to the emergence of such disciplines as associative typology, associative variantology, associative axiology; in expanding the spheres of applied use of psycholinguistics in social sciences, sociology, semasiology, lexicography, in the study of the brain, linguodidactics, medicine, etc. This book is a kind of summarizing result of the development of Russian psycholinguistics today. Each section provides a bibliography of studies on the relevant issue. The Appendix contains the scientometrics of leading Russian psycholinguists, basic monographs, psycholinguistic textbooks and dissertations defended in psycholinguistics. The content of the publications presented here is convincing evidence of the relevance of psycholinguistic topics and the effectiveness of the development of psycholinguistic problems in Russia.
Частини книг з теми "FEATURE OPTIMIZATION METHODS"
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Balavand, Alireza, and Soheyla Pahlevani. "Proposing a New Feature Clustering Method in Order to the Binary Classification of COVID-19 in Computed Tomography Images." In Engineering Optimization: Methods and Applications, 193–216. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1521-7_11.
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Mathieson, Luke, Alexandre Mendes, John Marsden, Jeffrey Pond, and Pablo Moscato. "Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets: Reduction Rules and Optimization Techniques." In Methods in Molecular Biology, 299–325. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6613-4_17.
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Bürger, Fabian, and Josef Pauli. "A Holistic Classification Optimization Framework with Feature Selection, Preprocessing, Manifold Learning and Classifiers." In Pattern Recognition: Applications and Methods, 52–68. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27677-9_4.
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Utracki, Jarosław, and Mariusz Boryczka. "Evolutionary and Aggressive Sampling for Pattern Revelation and Precognition in Building Energy Managing System with Nature-Based Methods for Energy Optimization." In Advances in Feature Selection for Data and Pattern Recognition, 295–319. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67588-6_15.
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Schneider, Lennart, Lennart Schäpermeier, Raphael Patrick Prager, Bernd Bischl, Heike Trautmann, and Pascal Kerschke. "HPO $$\times $$ ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis." In Lecture Notes in Computer Science, 575–89. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14714-2_40.
Повний текст джерелаАнотація:
AbstractHyperparameter optimization (HPO) is a key component of machine learning models for achieving peak predictive performance. While numerous methods and algorithms for HPO have been proposed over the last years, little progress has been made in illuminating and examining the actual structure of these black-box optimization problems. Exploratory landscape analysis (ELA) subsumes a set of techniques that can be used to gain knowledge about properties of unknown optimization problems. In this paper, we evaluate the performance of five different black-box optimizers on 30 HPO problems, which consist of two-, three- and five-dimensional continuous search spaces of the XGBoost learner trained on 10 different data sets. This is contrasted with the performance of the same optimizers evaluated on 360 problem instances from the black-box optimization benchmark (BBOB). We then compute ELA features on the HPO and BBOB problems and examine similarities and differences. A cluster analysis of the HPO and BBOB problems in ELA feature space allows us to identify how the HPO problems compare to the BBOB problems on a structural meta-level. We identify a subset of BBOB problems that are close to the HPO problems in ELA feature space and show that optimizer performance is comparably similar on these two sets of benchmark problems. We highlight open challenges of ELA for HPO and discuss potential directions of future research and applications.
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Garcia, Rodolfo, Emerson Cabrera Paraiso, and Júlio Cesar Nievola. "Multiobjective Optimization of Indexes Obtained by Clustering for Feature Selection Methods Evaluation in Genes Expression Microarrays." In Lecture Notes in Computer Science, 353–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23878-9_42.
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Wang, Jingbo, Yannan Li, and Chao Wang. "Synthesizing Fair Decision Trees via Iterative Constraint Solving." In Computer Aided Verification, 364–85. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13188-2_18.
Повний текст джерелаАнотація:
AbstractDecision trees are increasingly used to make socially sensitive decisions, where they are expected to be both accurate and fair, but it remains a challenging task to optimize the learning algorithm for fairness in a predictable and explainable fashion. To overcome the challenge, we propose an iterative framework for choosing decision attributes, or features, at each level by formulating feature selection as a series of mixed integer optimization problems. Both fairness and accuracy requirements are encoded as numerical constraints and solved by an off-the-shelf constraint solver. As a result, the trade-off between fairness and accuracy is quantifiable. At a high level, our method can be viewed as a generalization of the entropy-based greedy search techniques such as and , and existing fair learning techniques such as and . Our experimental evaluation on six datasets, for which demographic parity is used as the fairness metric, shows that the method is significantly more effective in reducing bias than other methods while maintaining accuracy. Furthermore, compared to non-iterative constraint solving, our iterative approach is at least 10 times faster.
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Figueira, Jose Rui, Salvatore Greco, Bernard Roy, and Roman Słowiński. "ELECTRE Methods: Main Features and Recent Developments." In Applied Optimization, 51–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-92828-7_3.
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Hernández, A. I., J. Dumont, M. Altuve, A. Beuchée, and G. Carrault. "Evolutionary Optimization of ECG Feature Extraction Methods: Applications to the Monitoring of Adult Myocardial Ischemia and Neonatal Apnea Bradycardia Events." In ECG Signal Processing, Classification and Interpretation, 237–73. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-868-3_11.
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Cheng, G., S. Meng, S. Liu, Y. Jiao, X. Chen, W. Zhang, H. Wen, W. Zhang, B. Wang, and X. Xu. "An Exploration into the Optimization of Feature Wavelength Screening Methods in the Processing of Frozen Fish Classification Data in Near Infrared Spectroscopy." In Sense the Real Change: Proceedings of the 20th International Conference on Near Infrared Spectroscopy, 97–107. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4884-8_9.
Повний текст джерелаТези доповідей конференцій з теми "FEATURE OPTIMIZATION METHODS"
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Talab, Mohammed Ahmed, Neven Ali Qahraman, Mais Muneam Aftan, Alaa Hamid Mohammed, and Mohd Dilshad Ansari. "Local Feature Methods Based Facial Recognition." In 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). IEEE, 2022. http://dx.doi.org/10.1109/hora55278.2022.9799910.
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Agarwal, Dheeraj, Simao Marques, Trevor T. Robinson, Cecil G. Armstrong, and Philip Hewitt. "Aerodynamic Shape Optimization Using Feature based CAD Systems and Adjoint Methods." In 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-3999.
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Prochazka, Michal, Zuzana Oplatkova, Jiri Holoska, and Vladimir Gerlich. "Optimization Of Neural Network Inputs By Feature Selection Methods." In 25th Conference on Modelling and Simulation. ECMS, 2011. http://dx.doi.org/10.7148/2011-0440-0445.
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Hussain, Chesti Altaff, D. Venkata Rao, and S. Aruna Mastani. "Low level feature extraction methods for Content Based Image Retrieval." In 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO). IEEE, 2015. http://dx.doi.org/10.1109/eesco.2015.7253924.
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Zhang, Jia, Yidong Lin, Min Jiang, Shaozi Li, Yong Tang, and Kay Chen Tan. "Multi-label Feature Selection via Global Relevance and Redundancy Optimization." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/348.
Повний текст джерелаАнотація:
Information theoretical based methods have attracted a great attention in recent years, and gained promising results to deal with multi-label data with high dimensionality. However, most of the existing methods are either directly transformed from heuristic single-label feature selection methods or inefficient in exploiting labeling information. Thus, they may not be able to get an optimal feature selection result shared by multiple labels. In this paper, we propose a general global optimization framework, in which feature relevance, label relevance (i.e., label correlation), and feature redundancy are taken into account, thus facilitating multi-label feature selection. Moreover, the proposed method has an excellent mechanism for utilizing inherent properties of multi-label learning. Specially, we provide a formulation to extend the proposed method with label-specific features. Empirical studies on twenty multi-label data sets reveal the effectiveness and efficiency of the proposed method. Our implementation of the proposed method is available online at: https://jiazhang-ml.pub/GRRO-master.zip.
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Griparis, Andreea, Daniela Faur, and Mihai Datcu. "Feature space dimensionality reduction for the optimization of visualization methods." In IGARSS 2015 - 2015 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2015. http://dx.doi.org/10.1109/igarss.2015.7325967.
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Bürger, Fabian, and Josef Pauli. "Representation Optimization with Feature Selection and Manifold Learning in a Holistic Classification Framework." In International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005183600350044.
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Özseven, Turgut, and Mustafa Arpacioglu. "Classification of Urban Sounds with PSO and WO Based Feature Selection Methods." In 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). IEEE, 2023. http://dx.doi.org/10.1109/hora58378.2023.10156803.
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Khurma, Ruba, Ibrahim Aljarah, and Ahmad Sharieh. "An Efficient Moth Flame Optimization Algorithm using Chaotic Maps for Feature Selection in the Medical Applications." In 9th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0008960701750182.
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Weber Martins, Thiago, and Reiner Anderl. "Feature Recognition and Parameterization Methods for Algorithm-Based Product Development Process." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67031.
Повний текст джерелаАнотація:
The algorithm-based product development process applies mathematical optimization tools in the conceptual steps of the product development process. It relies on formalized data such as initial loads and boundary conditions to find the best product solution for optimized bifurcated sheet metal parts. Previous research efforts focused on the automation of CAD modeling steps. Current algorithms are able to generate the CAD models of optimized bifurcated sheet metal products automatically, however, they are rough with low-level of detail and abstraction. Consequently, CAD models are embodied and detailed manually in a partly iterative and time-consuming process to include parameters, constraints and design features. Hence, this paper introduces feature recognition and parametrization methods for the algorithm-based product development of bifurcated sheet metal products. It proposes the inclusion of a pre-processor to analyze the solution graph resulted from topology optimization before the generation of CAD models. Algorithms that derive the geometric shape from the solution graph by recognizing features as well as assigning parameters are introduced. Then, feature-based CAD models of bifurcated sheet metal products are automatically generated. The proposed methods and algorithms are implemented with Python and validated with a use-case. Benefits and limitations of the proposed methods are discussed.
Звіти організацій з теми "FEATURE OPTIMIZATION METHODS"
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
Повний текст джерелаАнотація:
The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.