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Academic literature on the topic 'Selective classifier'

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Published: 11 March 2023

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Journal articles on the topic "Selective classifier"

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Pernkopf, Franz. "Bayesian network classifiers versus selective -NN classifier." Pattern Recognition 38, no. 1 (January 2005): 1–10. http://dx.doi.org/10.1016/j.patcog.2004.05.012.

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Wares, Scott, John Isaacs, and Eyad Elyan. "Burst Detection-Based Selective Classifier Resetting." Journal of Information & Knowledge Management 20, no. 02 (April 23, 2021): 2150027. http://dx.doi.org/10.1142/s0219649221500271.

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Concept drift detection algorithms have historically been faithful to the aged architecture of forcefully resetting the base classifiers for each detected drift. This approach prevents underlying classifiers becoming outdated as the distribution of a data stream shifts from one concept to another. In situations where both concept drift and temporal dependence are present within a data stream, forced resetting can cause complications in classifier evaluation. Resetting the base classifier too frequently when temporal dependence is present can cause classifier performance to appear successful, when in fact this is misleading. In this research, a novel architectural method for determining base classifier resets, Burst Detection-Based Selective Classifier Resetting (BD-SCR), is presented. BD-SCR statistically monitors changes in the temporal dependence of a data stream to determine if a base classifier should be reset for detected drifts. The experimental process compares the predictive performance of state-of-the-art drift detectors in comparison to the “No-Change” detector using BD-SCR to inform and control the resetting decision. Results show that BD-SCR effectively reduces the negative impact of temporal dependence during concept drift detection through a clear negation in the performance of the “No-Change” detector, but is capable of maintaining the predictive performance of state-of-the-art drift detection methods.
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Li, Kai, and Hong Tao Gao. "A Subgraph-Based Selective Classifier Ensemble Algorithm." Advanced Materials Research 219-220 (March 2011): 261–64. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.261.

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To improve the generalization performance for ensemble learning, a subgraph based selective classifier ensemble algorithm is presented. Firstly, a set of classifiers are generated by bootstrap sampling technique and support vector machine learning algorithm. And a complete undirected graph is constructed whose vertex is classifier and weight of edge between a pair of classifiers is diversity values. Secondly, by searching technique to find an edge with minimum weight and to calculate similarity values about two vertexes which is related to the edge, vertex with smaller similarity value is removed. According to this method, a subgraph is obtained. Finally, we choose vertexes of subgraph, i.e. classifiers, as ensemble members. Experiments show that presented method outperforms the traditional ensemble learning methods in classification accuracy.
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Wiener, Yair, and Ran El-Yaniv. "Agnostic Pointwise-Competitive Selective Classification." Journal of Artificial Intelligence Research 52 (January 26, 2015): 171–201. http://dx.doi.org/10.1613/jair.4439.

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Pointwise-competitive classifier from class F is required to classify identically to the best classifier in hindsight from F. For noisy, agnostic settings we present a strategy for learning pointwise-competitive classifiers from a finite training sample provided that the classifier can abstain from prediction at a certain region of its choice. For some interesting hypothesis classes and families of distributions, the measure of this rejected region is shown to be diminishing at a fast rate, with high probability. Exact implementation of the proposed learning strategy is dependent on an ERM oracle that can be hard to compute in the agnostic case. We thus consider a heuristic approximation procedure that is based on SVMs, and show empirically that this algorithm consistently outperforms a traditional rejection mechanism based on distance from decision boundary.
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Wang, Yan, Xiu Xia Wang, and Sheng Lai. "A Kind of Combination Feature Division and Diversity Measure of Multi-Classifier Selective Ensemble Algorithm." Applied Mechanics and Materials 63-64 (June 2011): 55–58. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.55.

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In ensemble learning, in order to improve the performance of individual classifiers and the diversity of classifiers, from the classifiers generation and combination, this paper proposes a kind of combination feature division and diversity measure of multi-classifier selective ensemble algorithm. The algorithm firstly applied bagging method to create some feature subsets, Secondly using principal component analysis of feature extraction method on each feature subsets, then select classifiers with high-classification accuracy; finally before classifier combination we use classifier diversity measure method select diversity classifiers. Experimental results prove that classification accuracy of the algorithm is obviously higher than popular bagging algorithm.
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Liu, Li Min, and Xiao Ping Fan. "A Survey: Clustering Ensemble Selection." Advanced Materials Research 403-408 (November 2011): 2760–63. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2760.

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Traditional clustering ensemble combines all of the available clustering partitions to get the final clustering result. But in supervised classification area,it has been known that selective classifier ensembles can always achieve better solutions.Following the selective classifier ensembles,the question of clustering ensemble is defined as clustering ensemble selection.The paper introduces the concept of clustering ensemble selection and gives the survey of clustering ensemble selection algorithms.
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Nikhar, Sonam, and A. M. Karandikar. "Prediction of Heart Disease Using Different Classification Techniques." APTIKOM Journal on Computer Science and Information Technologies 2, no. 2 (July 1, 2017): 68–76. http://dx.doi.org/10.11591/aptikom.j.csit.106.

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Data mining is one of the essential areas of research that is more popular in health organization. Heart disease is the leading cause of death in the world over the past 10 years. The healthcare industry gathers enormous amount of heart disease data which are not “mined” to discover hidden information for effective decision making. This research intends to provide a detailed description of Naïve Bayes, decision tree classifier and Selective Bayesian classifier that are applied in our research particularly in the prediction of Heart Disease. It is known that Naïve Bayesian classifier (NB) works very well on some domains, and poorly on some. The performance of NB suffers in domains that involve correlated features. C4.5 decision trees, on the other hand, typically perform better than the Naïve Bayesian algorithm on such domains. This paper describes a Selective Bayesian classifier (SBC) that simply uses only those features that C4.5 would use in its decision tree when learning a small example of a training set, a combination of the two different natures of classifiers. Experiments conducted on Cleveland datasets indicate that SBC performs reliably better than NB on all domains, and SBC outperforms C4.5 on this dataset of which C4.5 outperform NB. Some experiment has been conducted to compare the execution of predictive data mining technique on the same dataset, and the consequence reveals that Decision Tree outperforms over Bayesian classifier and experiment also reveals that selective Bayesian classifier has a better accuracy as compared to other classifiers.
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Tao, Xiaoling, Yong Wang, Yi Wei, and Ye Long. "Network Traffic Classification Based on Multi-Classifier Selective Ensemble." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 8, no. 2 (September 9, 2015): 88–94. http://dx.doi.org/10.2174/235209650802150909112547.

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Wei, Leyi, Shixiang Wan, Jiasheng Guo, and Kelvin KL Wong. "A novel hierarchical selective ensemble classifier with bioinformatics application." Artificial Intelligence in Medicine 83 (November 2017): 82–90. http://dx.doi.org/10.1016/j.artmed.2017.02.005.

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Zhang, Xiao Hua, Zhi Fei Liu, Ya Jun Guo, and Li Qiang Zhao. "Selective Facial Expression Recognition Using fastICA." Advanced Materials Research 433-440 (January 2012): 2755–61. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2755.

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This paper proposes a facial expression recognition approach based on the combination of fastICA method and neural network classifiers. First we get some special facial expression regions, including eyebrows, eyes and mouth, in which wavelet transform is done to reduce the dimension. Then the fastICA method is used to extract these three facial features. Finally, BP neural network classifier is adopted to recognize facial expression. Experimental on the JAFFE database results show that the method is effective for both dimension reduction and recognition performance in comparison with traditional PCA and ICA method. We have obtained recognition rates as high as 93.33% in categorizing the facial expressions neutral, anger, or sadness. The best average recognition rate achieves 90.48%.
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Dissertations / Theses on the topic "Selective classifier"

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Sayin, Günel Burcu. "Towards Reliable Hybrid Human-Machine Classifiers." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/349843.

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In this thesis, we focus on building reliable hybrid human-machine classifiers to be deployed in cost-sensitive classification tasks. The objective is to assess ML quality in hybrid classification contexts and design the appropriate metrics, thereby knowing whether we can trust the model predictions and identifying the subset of items on which the model is well-calibrated and trustworthy. We start by discussing the key concepts, research questions, challenges, and architecture to design and implement an effective hybrid classification service. We then present a deeper investigation of each service component along with our solutions and results. We mainly contribute to cost-sensitive hybrid classification, selective classification, model calibration, and active learning. We highlight the importance of model calibration in hybrid classification services and propose novel approaches to improve the calibration of human-machine classifiers. In addition, we argue that the current accuracy-based metrics are misaligned with the actual value of machine learning models and propose a novel metric ``value". We further test the performance of SOTA machine learning models in NLP tasks with a cost-sensitive hybrid classification context. We show that the performance of the SOTA models in cost-sensitive tasks significantly drops when we evaluate them according to value rather than accuracy. Finally, we investigate the quality of hybrid classifiers in the active learning scenarios. We review the existing active learning strategies, evaluate their effectiveness, and propose a novel value-aware active learning strategy to improve the performance of selective classifiers in the active learning of cost-sensitive tasks.
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BOLDT, F. A. "Classifier Ensemble Feature Selection for Automatic Fault Diagnosis." Universidade Federal do Espírito Santo, 2017. http://repositorio.ufes.br/handle/10/9872.

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"An efficient ensemble feature selection scheme applied for fault diagnosis is proposed, based on three hypothesis: a. A fault diagnosis system does not need to be restricted to a single feature extraction model, on the contrary, it should use as many feature models as possible, since the extracted features are potentially discriminative and the feature pooling is subsequently reduced with feature selection; b. The feature selection process can be accelerated, without loss of classification performance, combining feature selection methods, in a way that faster and weaker methods reduce the number of potentially non-discriminative features, sending to slower and stronger methods a filtered smaller feature set; c. The optimal feature set for a multi-class problem might be different for each pair of classes. Therefore, the feature selection should be done using an one versus one scheme, even when multi-class classifiers are used. However, since the number of classifiers grows exponentially to the number of the classes, expensive techniques like Error-Correcting Output Codes (ECOC) might have a prohibitive computational cost for large datasets. Thus, a fast one versus one approach must be used to alleviate such a computational demand. These three hypothesis are corroborated by experiments. The main hypothesis of this work is that using these three approaches together is possible to improve significantly the classification performance of a classifier to identify conditions in industrial processes. Experiments have shown such an improvement for the 1-NN classifier in industrial processes used as case study."
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Thapa, Mandira. "Optimal Feature Selection for Spatial Histogram Classifiers." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1513710294627304.

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Gustafsson, Robin. "Ordering Classifier Chains using filter model feature selection techniques." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14817.

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Context: Multi-label classification concerns classification with multi-dimensional output. The Classifier Chain breaks the multi-label problem into multiple binary classification problems, chaining the classifiers to exploit dependencies between labels. Consequently, its performance is influenced by the chain's order. Approaches to finding advantageous chain orders have been proposed, though they are typically costly. Objectives: This study explored the use of filter model feature selection techniques to order Classifier Chains. It examined how feature selection techniques can be adapted to evaluate label dependence, how such information can be used to select a chain order and how this affects the classifier's performance and execution time. Methods: An experiment was performed to evaluate the proposed approach. The two proposed algorithms, Forward-Oriented Chain Selection (FOCS) and Backward-Oriented Chain Selection (BOCS), were tested with three different feature evaluators. 10-fold cross-validation was performed on ten benchmark datasets. Performance was measured in accuracy, 0/1 subset accuracy and Hamming loss. Execution time was measured during chain selection, classifier training and testing. Results: Both proposed algorithms led to improved accuracy and 0/1 subset accuracy (Friedman & Hochberg, p < 0.05). FOCS also improved the Hamming loss while BOCS did not. Measured effect sizes ranged from 0.20 to 1.85 percentage points. Execution time was increased by less than 3 % in most cases. Conclusions: The results showed that the proposed approach can improve the Classifier Chain's performance at a low cost. The improvements appear similar to comparable techniques in magnitude but at a lower cost. It shows that feature selection techniques can be applied to chain ordering, demonstrates the viability of the approach and establishes FOCS and BOCS as alternatives worthy of further consideration.
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Duangsoithong, Rakkrit. "Feature selection and casual discovery for ensemble classifiers." Thesis, University of Surrey, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.580345.

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With rapid development of computer and information technology that can improve a large number of applications such as web text mining, intrusion detection, biomedical informatics, gene selection in micro array data, medical data mining, and clinical de- cision support systems, many information databases have been created. However, in some applications especially in the medical area, clinical data may contain hundreds to thousands of features with relatively few samples. A consequence of this problem is increased complexity that leads to degradation in efficiency and accuracy. Moreover, in this high dimensional feature space, many features are possibly irrelevant or redundant and should be removed in order to ensure good generalisation performance. Otherwise, the classifier may over-fit the data, that is the classifier may specialise on features which are not relevant for discrimination. To overcome this problem, feature selection and ensemble classification are applied. In this thesis, an empirical analysis on using bootstrap and random subspace feature selection for multiple classifier system is investigated and bootstrap feature selection and embedded feature ranking for ensemble MLP classifiers along with a stopping criterion based on the out-of-bootstrap estimate are proposed. Moreover, basically, feature selection does not usually take causal discovery into ac- count. However, in some cases such as when the testing distribution is shifted from manipulation by external agent, causal discovery can provide some benefits for feature selection under these uncertainty conditions. It also can learn the underlying data structure, provide better understanding of the data generation process and better accuracy and robustness under uncertainty. Similarly, feature selection mutually enables global causal discovery algorithms to deal with high dimensional data by eliminating irrelevant and redundant features before exploring the causal relationship between features. A redundancy-based ensemble causal feature selection approach using bootstrap and random subspace and a comparison between correlation-based and causal feature selection for ensemble classifiers are analysed. Finally, hybrid correlation-causal feature selection for multiple classifier system is proposed in order to scale up causal discovery and deal with high dimensional features.
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Ko, Albert Hung-Ren. "Static and dynamic selection of ensemble of classifiers." Thèse, Montréal : École de technologie supérieure, 2007. http://proquest.umi.com/pqdweb?did=1467895171&sid=2&Fmt=2&clientId=46962&RQT=309&VName=PQD.

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Thèse (Ph.D.) -- École de technologie supérieure, Montréal, 2007.
"A thesis presented to the École de technologie supérieure in partial fulfillment of the thesis requirement for the degree of the Ph.D. engineering". CaQMUQET Bibliogr. : f. [237]-246. Également disponible en version électronique. CaQMUQET
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McCrae, Richard. "The Impact of Cost on Feature Selection for Classifiers." Thesis, Nova Southeastern University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=13423087.

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Supervised machine learning models are increasingly being used for medical diagnosis. The diagnostic problem is formulated as a binary classification task in which trained classifiers make predictions based on a set of input features. In diagnosis, these features are typically procedures or tests with associated costs. The cost of applying a trained classifier for diagnosis may be estimated as the total cost of obtaining values for the features that serve as inputs for the classifier. Obtaining classifiers based on a low cost set of input features with acceptable classification accuracy is of interest to practitioners and researchers. What makes this problem even more challenging is that costs associated with features vary with patients and service providers and change over time.

This dissertation aims to address this problem by proposing a method for obtaining low cost classifiers that meet specified accuracy requirements under dynamically changing costs. Given a set of relevant input features and accuracy requirements, the goal is to identify all qualifying classifiers based on subsets of the feature set. Then, for any arbitrary costs associated with the features, the cost of the classifiers may be computed and candidate classifiers selected based on cost-accuracy tradeoff. Since the number of relevant input features k tends to be large for typical diagnosis problems, training and testing classifiers based on all 2k – 1 possible non-empty subsets of features is computationally prohibitive. Under the reasonable assumption that the accuracy of a classifier is no lower than that of any classifier based on a subset of its input features, this dissertation aims to develop an efficient method to identify all qualifying classifiers.

This study used two types of classifiers—artificial neural networks and classification trees—that have proved promising for numerous problems as documented in the literature. The approach was to measure the accuracy obtained with the classifiers when all features were used. Then, reduced thresholds of accuracy were arbitrarily established which were satisfied with subsets of the complete feature set. Threshold values for three measures—true positive rates, true negative rates, and overall classification accuracy were considered for the classifiers. Two cost functions were used for the features; one used unit costs and the other random costs. Additional manipulation of costs was also performed.

The order in which features were removed was found to have a material impact on the effort required (removing the most important features first was most efficient, removing the least important features first was least efficient). The accuracy and cost measures were combined to produce a Pareto-Optimal Frontier. There were consistently few elements on this Frontier. At most 15 subsets were on the Frontier even when there were hundreds of thousands of acceptable feature sets. Most of the computational time is taken for training and testing the models. Given costs, models in the Pareto-Optimal Frontier can be efficiently identified and the models may be presented to decision makers. Both the Neural Networks and the Decision Trees performed in a comparable fashion suggesting that any classifier could be employed.

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McCrae, Richard Clyde. "The Impact of Cost on Feature Selection for Classifiers." Diss., NSUWorks, 2018. https://nsuworks.nova.edu/gscis_etd/1057.

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Supervised machine learning models are increasingly being used for medical diagnosis. The diagnostic problem is formulated as a binary classification task in which trained classifiers make predictions based on a set of input features. In diagnosis, these features are typically procedures or tests with associated costs. The cost of applying a trained classifier for diagnosis may be estimated as the total cost of obtaining values for the features that serve as inputs for the classifier. Obtaining classifiers based on a low cost set of input features with acceptable classification accuracy is of interest to practitioners and researchers. What makes this problem even more challenging is that costs associated with features vary with patients and service providers and change over time. This dissertation aims to address this problem by proposing a method for obtaining low cost classifiers that meet specified accuracy requirements under dynamically changing costs. Given a set of relevant input features and accuracy requirements, the goal is to identify all qualifying classifiers based on subsets of the feature set. Then, for any arbitrary costs associated with the features, the cost of the classifiers may be computed and candidate classifiers selected based on cost-accuracy tradeoff. Since the number of relevant input features k tends to be large for typical diagnosis problems, training and testing classifiers based on all 2^k-1 possible non-empty subsets of features is computationally prohibitive. Under the reasonable assumption that the accuracy of a classifier is no lower than that of any classifier based on a subset of its input features, this dissertation aims to develop an efficient method to identify all qualifying classifiers. This study used two types of classifiers – artificial neural networks and classification trees – that have proved promising for numerous problems as documented in the literature. The approach was to measure the accuracy obtained with the classifiers when all features were used. Then, reduced thresholds of accuracy were arbitrarily established which were satisfied with subsets of the complete feature set. Threshold values for three measures –true positive rates, true negative rates, and overall classification accuracy were considered for the classifiers. Two cost functions were used for the features; one used unit costs and the other random costs. Additional manipulation of costs was also performed. The order in which features were removed was found to have a material impact on the effort required (removing the most important features first was most efficient, removing the least important features first was least efficient). The accuracy and cost measures were combined to produce a Pareto-Optimal Frontier. There were consistently few elements on this Frontier. At most 15 subsets were on the Frontier even when there were hundreds of thousands of acceptable feature sets. Most of the computational time is taken for training and testing the models. Given costs, models in the Pareto-Optimal Frontier can be efficiently identified and the models may be presented to decision makers. Both the Neural Networks and the Decision Trees performed in a comparable fashion suggesting that any classifier could be employed.
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Pinagé, Felipe Azevedo, and 92-98187-1016. "Handling Concept Drift Based on Data Similarity and Dynamic Classifier Selection." Universidade Federal do Amazonas, 2017. http://tede.ufam.edu.br/handle/tede/5956.

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FAPEAM - Fundação de Amparo à Pesquisa do Estado do Amazonas
In real-world applications, machine learning algorithms can be employed to perform spam detection, environmental monitoring, fraud detection, web click stream, among others. Most of these problems present an environment that changes over time due to the dynamic generation process of the data and/or due to streaming data. The problem involving classification tasks of continuous data streams has become one of the major challenges of the machine learning domain in the last decades because, since data is not known in advance, it must be learned as it becomes available. In addition, fast predictions about data should be performed to support often real time decisions. Currently in the literature, methods based on accuracy monitoring are commonly used to detect changes explicitly. However, these methods may become infeasible in some real-world applications especially due to two aspects: they may need human operator feedback, and may depend on a significant decrease of accuracy to be able to detect changes. In addition, most of these methods are also incremental learning-based, since they update the decision model for every incoming example. However, this may lead the system to unnecessary updates. In order to overcome these problems, in this thesis, two semi-supervised methods based on estimating and monitoring a pseudo error are proposed to detect changes explicitly. The decision model is updated only after changing detection. In the first method, the pseudo error is calculated using similarity measures by monitoring the dissimilarity between past and current data distributions. The second proposed method employs dynamic classifier selection in order to improve the pseudo error measurement. As a consequence, this second method allows classifier ensemble online self-training. The experiments conducted show that the proposed methods achieve competitive results, even when compared to fully supervised incremental learning methods. The achievement of these methods, especially the second method, is relevant since they lead change detection and reaction to be applicable in several practical problems reaching high accuracy rates, where usually is not possible to generate the true labels of the instances fully and immediately after classification.
Em aplicações do mundo real, algoritmos de aprendizagem de máquina podem ser usados para detecção de spam, monitoramento ambiental, detecção de fraude, fluxo de cliques na Web, dentre outros. A maioria desses problemas apresenta ambientes que sofrem mudanças com o passar do tempo, devido à natureza dinâmica de geração dos dados e/ou porque envolvem dados que ocorrem em fluxo. O problema envolvendo tarefas de classificação em fluxo contínuo de dados tem se tornado um dos maiores desafios na área de aprendizagem de máquina nas últimas décadas, pois, como os dados não são conhecidos de antemão, eles devem ser aprendidos à medida que são processados. Além disso, devem ser feitas previsões rápidas a respeito desses dados para dar suporte à decisões muitas vezes tomadas em tempo real. Atualmente, métodos baseados em monitoramento da acurácia de classificação são geralmente usados para detectar explicitamente mudanças nos dados. Entretanto, esses métodos podem tornar-se inviáveis em aplicações práticas, especialmente devido a dois aspectos: a necessidade de uma realimentação do sistema por um operador humano, e a dependência de uma queda significativa da acurácia para que mudanças sejam detectadas. Além disso, a maioria desses métodos é baseada em aprendizagem incremental, onde modelos de predição são atualizados para cada instância de entrada, fato que pode levar a atualizações desnecessárias do sistema. A fim de tentar superar todos esses problemas, nesta tese são propostos dois métodos semi-supervisionados de detecção explícita de mudanças em dados, os quais baseiam-se na estimação e monitoramento de uma métrica de pseudo-erro. O modelo de decisão é atualizado somente após a detecção de uma mudança. No primeiro método proposto, o pseudo-erro é monitorado a partir de métricas de similaridade calculadas entre a distribuição atual e distribuições anteriores dos dados. O segundo método proposto utiliza seleção dinâmica de classificadores para aumentar a precisão do cálculo do pseudo-erro. Como consequência, nosso método possibilita que conjuntos de classificadores online sejam criados a partir de auto-treinamento. Os experimentos apresentaram resultados competitivos quando comparados inclusive com métodos baseados em aprendizagem incremental totalmente supervisionada. A proposta desses dois métodos, especialmente do segundo, é relevante por permitir que tarefas de detecção e reação a mudanças sejam aplicáveis em diversos problemas práticos alcançando altas taxas de acurácia, dado que, na maioria dos problemas práticos, não é possível obter o rótulo de uma instância imediatamente após sua classificação feita pelo sistema.
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デイビッド, ア., and David Ha. "Boundary uncertainty-based classifier evaluation." Thesis, https://doors.doshisha.ac.jp/opac/opac_link/bibid/BB13128126/?lang=0, 2019. https://doors.doshisha.ac.jp/opac/opac_link/bibid/BB13128126/?lang=0.

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種々の分類器を対象として，有限個の学習データのみが利用可能である現実においても理論的に的確で計算量的にも実際的な，分類器性能評価手法を提案する．分類器評価における難しさは，有限データのみの利用に起因する分類誤り推定に伴う偏りの発生にある．この困難を解決するため，「境界曖昧性」と呼ばれる新しい評価尺度を提案し，それを用いる評価法の有用性を，3種の分類器と13個のデータセットを用いた実験を通して実証する．
We propose a general method that makes accurate evaluation of any classifier model for realistic tasks, both in a theoretical sense despite the finiteness of the available data, and in a practical sense in terms of computation costs. The classifier evaluation challenge arises from the bias of the classification error estimate that is only based on finite data. We bypass this existing difficulty by proposing a new classifier evaluation measure called "boundary uncertainty'' whose estimate based on finite data can be considered a reliable representative of its expectation based on infinite data, and demonstrate the potential of our approach on three classifier models and thirteen datasets.
博士(工学)
Doctor of Philosophy in Engineering
同志社大学
Doshisha University
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Books on the topic "Selective classifier"

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B, Krimmel Michael, and Hartz Emilie K, eds. Prison librarianship: A selective, annotated, classified bibliography, 1945-1985. Jefferson, N.C: McFarland, 1987.

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Mitchell, Alastair. Classified selective list of reading and other published material for the community worker. 2nd ed. London: National Federation of Community Organisations, 1988.

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Ridgway, Peggi. Romancing in the personal ads: How to find your partner in the classifieds. La Mirada, CA: Wordpictures, 1996.

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Broom, Herbert. Selection of Legal Maxims: Classified and Illustrated. Creative Media Partners, LLC, 2018.

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Broom, Herbert. Selection of Legal Maxims, Classified and Illustrated. Creative Media Partners, LLC, 2018.

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Broom, Herbert. Selection of Legal Maxims: Classified and Illustrated. Creative Media Partners, LLC, 2018.

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Selection of Legal Maxims: Classified and Illustrated. Creative Media Partners, LLC, 2022.

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Selection of Legal Maxims: Classified and Illustrated. Creative Media Partners, LLC, 2022.

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Broom, Herbert. Selection of Legal Maxims, Classified and Illustrated. Creative Media Partners, LLC, 2018.

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Broom, Herbert. A Selection of Legal Maxims: Classified and Illustrated. Franklin Classics, 2018.

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Book chapters on the topic "Selective classifier"

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Li, Nan, Yuan Jiang, and Zhi-Hua Zhou. "Multi-label Selective Ensemble." In Multiple Classifier Systems, 76–88. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20248-8_7.

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Li, Nan, and Zhi-Hua Zhou. "Selective Ensemble under Regularization Framework." In Multiple Classifier Systems, 293–303. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02326-2_30.

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Li, Nan, and Zhi-Hua Zhou. "Selective Ensemble of Classifier Chains." In Multiple Classifier Systems, 146–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38067-9_13.

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Lu, Xuyao, Yan Yang, and Hongjun Wang. "Selective Clustering Ensemble Based on Covariance." In Multiple Classifier Systems, 179–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38067-9_16.

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Krasotkina, Olga, Oleg Seredin, and Vadim Mottl. "Supervised Selective Combination of Diverse Object-Representation Modalities for Regression Estimation." In Multiple Classifier Systems, 89–99. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20248-8_8.

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Tatarchuk, Alexander, Eugene Urlov, Vadim Mottl, and David Windridge. "A Support Kernel Machine for Supervised Selective Combining of Diverse Pattern-Recognition Modalities." In Multiple Classifier Systems, 165–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12127-2_17.

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Tatarchuk, Alexander, Valentina Sulimova, David Windridge, Vadim Mottl, and Mikhail Lange. "Supervised Selective Combining Pattern Recognition Modalities and Its Application to Signature Verification by Fusing On-Line and Off-Line Kernels." In Multiple Classifier Systems, 324–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02326-2_33.

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Velasco, Horacio M. González, Carlos J. García Orellana, Miguel Macías Macías, and Ramón Gallardo Caballero. "Selective Color Edge Detector Based on a Neural Classifier." In Advanced Concepts for Intelligent Vision Systems, 84–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11558484_11.

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Ashwini, S. S., M. Z. Kurian, and M. Nagaraja. "Lung Cancer Detection and Prediction Using Customized Selective Segmentation Technique with SVM Classifier." In Emerging Research in Computing, Information, Communication and Applications, 37–44. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1342-5_4.

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Hue, Carine, Marc Boullé, and Vincent Lemaire. "Online Learning of a Weighted Selective Naive Bayes Classifier with Non-convex Optimization." In Advances in Knowledge Discovery and Management, 3–17. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45763-5_1.

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Conference papers on the topic "Selective classifier"

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Germi, Saeed Bakhshi, Esa Rahtu, and Heikki Huttunen. "Selective Probabilistic Classifier Based on Hypothesis Testing." In 2021 9th European Workshop on Visual Information Processing (EUVIP). IEEE, 2021. http://dx.doi.org/10.1109/euvip50544.2021.9483967.

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Ahmad, Irshad, Abdul Muhamin Naeem, Muhammad Islam, and Azween Bin Abdullah. "Statistical Based Real-Time Selective Herbicide Weed Classifier." In 2007 IEEE International Multitopic Conference (INMIC). IEEE, 2007. http://dx.doi.org/10.1109/inmic.2007.4557689.

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Chen, Jingnian, and Li Xu. "A Hybrid Selective Classifier for Categorizing Incomplete Data." In 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, 2009. http://dx.doi.org/10.1109/fskd.2009.257.

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Fan, Yawen, Husheng Li, and Chao Tian. "Selective Sampling Based Efficient Classifier Representation in Distributed Learning." In GLOBECOM 2016 - 2016 IEEE Global Communications Conference. IEEE, 2016. http://dx.doi.org/10.1109/glocom.2016.7842257.

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Ning, Bo, XianBin Cao, YanWu Xu, and Jun Zhang. "Virus-evolutionary genetic algorithm based selective ensemble classifier for pedestrian detection." In the first ACM/SIGEVO Summit. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1543834.1543893.

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Boulle, M. "Regularization and Averaging of the Selective Na&#239;ve Bayes classifier." In The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.246637.

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Bai, Lixia, Hong Li, and Weifeng Gao. "A Selective Ensemble Classifier Using Multiobjective Optimization Based Extreme Learning Machine Algorithm." In 2021 17th International Conference on Computational Intelligence and Security (CIS). IEEE, 2021. http://dx.doi.org/10.1109/cis54983.2021.00017.

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Ortiz-Bayliss, Jose C., Hugo Terashima-Marin, and Santiago E. Conant-Pablos. "Using learning classifier systems to design selective hyper-heuristics for constraint satisfaction problems." In 2013 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2013. http://dx.doi.org/10.1109/cec.2013.6557885.

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Honda, Toshifumi, Ryo Nakagaki, Obara Kenji, and Yuji Takagi. "Fuzzy selective voting classifier with defect extraction based on comparison within an image." In International Symposium on Multispectral Image Processing and Pattern Recognition, edited by S. J. Maybank, Mingyue Ding, F. Wahl, and Yaoting Zhu. SPIE, 2007. http://dx.doi.org/10.1117/12.750528.

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Balasubramanian, Ram, M. A. El-Sharkawi, R. J. Marks, Jae-Byung Jung, R. T. Miyamoto, G. M. Andersen, C. J. Eggen, and W. L. J. Fox. "Self-selective clustering of training data using the maximally-receptive classifier/regression bank." In 2009 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2009. http://dx.doi.org/10.1109/icsmc.2009.5346820.

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Reports on the topic "Selective classifier"

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Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568747.bard.

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This project includes two main parts: Development of a “Selective Wavelength Imaging Sensor” and an “Adaptive Classifiery System” for adaptive imaging and sorting of agricultural products respectively. Three different technologies were investigated for building a selectable wavelength imaging sensor: diffraction gratings, tunable filters and linear variable filters. Each technology was analyzed and evaluated as the basis for implementing the adaptive sensor. Acousto optic tunable filters were found to be most suitable for the selective wavelength imaging sensor. Consequently, a selectable wavelength imaging sensor was constructed and tested using the selected technology. The sensor was tested and algorithms for multispectral image acquisition were developed. A high speed inspection system for fresh-market carrots was built and tested. It was shown that a combination of efficient parallel processing of a DSP and a PC based host CPU in conjunction with a hierarchical classification system, yielded an inspection system capable of handling 2 carrots per second with a classification accuracy of more than 90%. The adaptive sorting technique was extensively investigated and conclusively demonstrated to reduce misclassification rates in comparison to conventional non-adaptive sorting. The adaptive classifier algorithm was modeled and reduced to a series of modules that can be added to any existing produce sorting machine. A simulation of the entire process was created in Matlab using a graphical user interface technique to promote the accessibility of the difficult theoretical subjects. Typical Grade classifiers based on k-Nearest Neighbor techniques and linear discriminants were implemented. The sample histogram, estimating the cumulative distribution function (CDF), was chosen as a characterizing feature of prototype populations, whereby the Kolmogorov-Smirnov statistic was employed as a population classifier. Simulations were run on artificial data with two-dimensions, four populations and three classes. A quantitative analysis of the adaptive classifier's dependence on population separation, training set size, and stack length determined optimal values for the different parameters involved. The technique was also applied to a real produce sorting problem, e.g. an automatic machine for sorting dates by machine vision in an Israeli date packinghouse. Extensive simulations were run on actual sorting data of dates collected over a 4 month period. In all cases, the results showed a clear reduction in classification error by using the adaptive technique versus non-adaptive sorting.
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Webb, Geoffrey, and Mark Carman. Dynamic Dimensionality Selection for Bayesian Classifier Ensembles. Fort Belvoir, VA: Defense Technical Information Center, March 2015. http://dx.doi.org/10.21236/ada614917.

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Zchori-Fein, Einat, Judith K. Brown, and Nurit Katzir. Biocomplexity and Selective modulation of whitefly symbiotic composition. United States Department of Agriculture, June 2006. http://dx.doi.org/10.32747/2006.7591733.bard.

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Whiteflies are sap-sucking insects that harbor obligatory symbiotic bacteria to fulfill their dietary needs, as well as a facultative microbial community with diverse bacterial species. The sweetpotato whitefly Bemisia tabaci (Gennadius) is a severe agricultural pest in many parts of the world. This speciesconsists of several biotypes that have been distinguished largely on the basis of biochemical or molecular diagnostics, but whose biological significance is still unclear. The original objectives of the project were (i) to identify the specific complement of prokaryotic endosymbionts associated with select, well-studied, biologically and phylogeographically representative biotypes of B. tabaci, and (ii) to attempt to 'cure’ select biotypes of certain symbionts to permit assessment of the affect of curing on whitefly fitness, gene flow, host plant preference, and virus transmission competency.To identify the diversity of bacterial community associated with a suite of phylogeographically-diverseB. tabaci, a total of 107 populations were screened using general Bacteria primers for the 16S rRNA encoding gene in a PCR. Sequence comparisons with the available databases revealed the presence of bacteria classified in the: Proteobacteria (66%), Firmicutes (25.70%), Actinobacteria (3.7%), Chlamydiae (2.75%) and Bacteroidetes (<1%). Among previously identified bacteria, such as the primary symbiont Portiera aleyrodidarum, and the secondary symbionts Hamiltonella, Cardinium and Wolbachia, a Rickettsia sp. was detected for the first time in this insect family. The distribution, transmission, and localization of the Rickettsia were studied using PCR and fluorescence in situ hybridization (FISH). Rickettsia was found in all 20 Israeli B. tabaci populations screened as well as some populations screened in the Arizona laboratory, but not in all individuals within each population. FISH analysis of B. tabaci eggs, nymphs and adults, revealed a unique concentration of Rickettsia around the gut and follicle cells as well as its random distribution in the haemolymph, but absence from the primary symbiont housing cells, the bacteriocytes. Rickettsia vertical transmission on the one hand and its partial within-population infection on the other suggest a phenotype that is advantageous under certain conditions but may be deleterious enough to prevent fixation under others.To test for the possible involvement of Wolbachia and Cardiniumin the reproductive isolation of different B. tabacibiotypes, reciprocal crosses were preformed among populations of the Cardinium-infected, Wolbachia-infected and uninfected populations. The crosses results demonstrated that phylogeographically divergent B. tabaci are reproductively competent and that cytoplasmic incompatibility inducer-bacteria (Wolbachia and Cardinium) both interfered with, and/or rescued CI induced by one another, effectively facilitating bidirectional female offspring production in the latter scenario.This knowledge has implications to multitrophic interactions, gene flow, speciation, fitness, natural enemy interactions, and possibly, host preference and virus transmission. Although extensive and creative attempts undertaken in both laboratories to cure whiteflies of non-primary symbionts have failed, our finding of naturally uninfected individuals have permitted the establishment of Rickettsia-, Wolbachia- and Cardinium-freeB. tabaci lines, which are been employed to address various biological questions, including determining the role of these bacteria in whitefly host biology.
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Dzanku, Fred M., and Louis S. Hodey. Achieving Inclusive Oil Palm Commercialisation in Ghana. Institute of Development Studies (IDS), February 2022. http://dx.doi.org/10.19088/apra.2022.007.

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Oil palm is the most important export crop in Ghana, aside from cocoa. Compared with cocoa, however, oil palm has a more extensive local value chain, including greater opportunity for local industrial and artisanal processing into palm oil and other products, which creates a high potential for employment generation and poverty reduction; as a result oil palm is classified as a priority crop. The selection of oil palm as a priority crop aims to promote agricultural commercialisation through domestic agroindustry development and exports. In spite of this, the oil palm economy has still not achieved its potential, and this begs the question, why? Although it is known in general that commercialisation potential and its benefits are not equally distributed across groups, it is not clear how and why different subgroups (women, men, youth) might benefit differently from the oil palm economy. This brief addresses why different groups of smallholders (women, men, youth) benefit unequally from oil palm value chains, and how returns to oil palm production and marketing could become more inclusive.
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Zhao, Bingyu, Saul Burdman, Ronald Walcott, Tal Pupko, and Gregory Welbaum. Identifying pathogenic determinants of Acidovorax citrulli toward the control of bacterial fruit blotch of cucurbits. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598168.bard.

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The specific objectives of this BARD proposal were: Use a comparative genomics approach to identify T3Es in group I, II and III strains of A. citrulli. Determine the bacterial genes contributing to host preference. Develop mutant strains that can be used for biological control of BFB. Background to the topic: Bacterial fruit blotch (BFB) of cucurbits, caused by Acidovoraxcitrulli, is a devastating disease that affects watermelon (Citrulluslanatus) and melon (Cucumismelo) production worldwide, including both Israel and USA. Three major groups of A. citrullistrains have been classified based on their virulence on host plants, genetics and biochemical properties. The host selection could be one of the major factors that shape A. citrullivirulence. The differences in the repertoire of type III‐ secreted effectors (T3Es) among the three A. citrulligroups could play a major role in determining host preferential association. Currently, there are only 11 A. citrulliT3Es predicted by the annotation of the genome of the group II strain, AAC00‐1. We expect that new A. citrulliT3Es can be identified by a combination of bioinformatics and experimental approaches, which may help us to further define the relationship of T3Es and host preference of A. citrulli. Implications, both scientific and agricultural: Enriching the information on virulence and avirulence functions of T3Es will contribute to the understanding of basic aspects of A. citrulli‐cucurbit interactions. In the long term, it will contribute to the development of durable BFB resistance in commercial varieties. In the short term, identifying bacterial genes that contribute to virulence and host preference will allow the engineering of A. citrullimutants that can trigger SAR in a given host. If applied as seed treatments, these should significantly improve the effectiveness and efficacy of BFB management in melon and atermelon production.
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Brosh, Arieh, Gordon Carstens, Kristen Johnson, Ariel Shabtay, Joshuah Miron, Yoav Aharoni, Luis Tedeschi, and Ilan Halachmi. Enhancing Sustainability of Cattle Production Systems through Discovery of Biomarkers for Feed Efficiency. United States Department of Agriculture, July 2011. http://dx.doi.org/10.32747/2011.7592644.bard.

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Feed inputs represent the largest variable cost of producing meat and milk from ruminant animals. Thus, strategies that improve the efficiency of feed utilization are needed to improve the global competitiveness of Israeli and U.S. cattle industries, and mitigate their environmental impact through reductions in nutrient excretions and greenhouse gas emissions. Implementation of innovative technologies that will enhance genetic merit for feed efficiency is arguably one of the most cost-effective strategies to meet future demands for animal-protein foods in an environmentally sustainable manner. While considerable genetic variation in feed efficiency exist within cattle populations, the expense of measuring individual-animal feed intake has precluded implementation of selection programs that target this trait. Residual feed intake (RFI) is a trait that quantifies between-animal variation in feed intake beyond that expected to meet energy requirements for maintenance and production, with efficient animals being those that eat less than expected for a given size and level of production. There remains a critical need to understand the biological drivers for genetic variation in RFI to facilitate development of effective selection programs in the future. Therefore, the aim of this project was to determine the biological basis for phenotypic variation in RFI of growing and lactating cattle, and discover metabolic biomarkers of RFI for early and more cost-effective selection of cattle for feed efficiency. Objectives were to: (1) Characterize the phenotypic relationships between RFI and production traits (growth or lactation), (2) Quantify inter-animal variation in residual HP, (3) Determine if divergent RFIphenotypes differ in HP, residual HP, recovered energy and digestibility, and (4) Determine if divergent RFI phenotypes differ in physical activity, feeding behavior traits, serum hormones and metabolites and hepatic mitochondrial traits. The major research findings from this project to date include: In lactating dairy cattle, substantial phenotypic variation in RFI was demonstrated as cows classified as having low RMEI consumed 17% less MEI than high-RMEI cows despite having similar body size and lactation productivity. Further, between-animal variation in RMEI was found to moderately associated with differences in RHP demonstrating that maintenance energy requirements contribute to observed differences in RFI. Quantifying energetic efficiency of dairy cows using RHP revealed that substantial changes occur as week of lactation advances—thus it will be critical to measure RMEI at a standardized stage of lactation. Finally, to determine RMEI in lactating dairy cows, individual DMI and production data should be collected for a minimum of 6 wk. We demonstrated that a favorably association exists between RFI in growing heifers and efficiency of forage utilization in pregnant cows. Therefore, results indicate that female progeny from parents selected for low RFI during postweaning development will also be efficient as mature females, which has positive implications for both dairy and beef cattle industries. Results from the beef cattle studies further extend our knowledge regarding the biological drivers of phenotypic variation in RFI of growing animals, and demonstrate that significant differences in feeding behavioral patterns, digestibility and heart rate exist between animals with divergent RFI. Feeding behavior traits may be an effective biomarker trait for RFI in beef and dairy cattle. There are differences in mitochondrial acceptor control and respiratory control ratios between calves with divergent RFI suggesting that variation in mitochondrial metabolism may be visible at the genome level. Multiple genes associated with mitochondrial energy processes are altered by RFI phenotype and some of these genes are associated with mitochondrial energy expenditure and major cellular pathways involved in regulation of immune responses and energy metabolism.
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Multiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0616.

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As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of extracting signal components. Therefore, a method based on spectral energy distribution is proposed to determine the parameter, and the quadratic penalty term is optimized according to SNR. The results show that the optimized VMD can adaptively extract the vibration signal components of the diesel engine. In the actual fault diagnosis case, it is difficult to obtain the data with labels. The clustering algorithm can complete the classification without labeled data, but it is limited by the low accuracy. In this paper, the optimized VMD is used to decompose and standardize the vibration signal. Then the correlation-based feature selection method is implemented to obtain the feature results after dimensionality reduction. Finally, the results are input into the classifier combined by K-means and genetic algorithm (GA). By introducing and optimizing the genetic algorithm, the number of classes can be selected automatically, and the accuracy is significantly improved. This method can carry out adaptive multiple fault detection of a diesel engine without labeled data. Compared with many supervised learning algorithms, the proposed method also has high accuracy.
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