Relevant bibliographies by topics / Evaluation of extreme classifiers

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Academic literature on the topic 'Evaluation of extreme classifiers'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Evaluation of extreme classifiers"

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Balasubramanian, Kishore, and N. P. Ananthamoorthy. "Analysis of hybrid statistical textural and intensity features to discriminate retinal abnormalities through classifiers." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 233, no. 5 (March 20, 2019): 506–14. http://dx.doi.org/10.1177/0954411919835856.

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Retinal image analysis relies on the effectiveness of computational techniques to discriminate various abnormalities in the eye like diabetic retinopathy, macular degeneration and glaucoma. The onset of the disease is often unnoticed in case of glaucoma, the effect of which is felt only at a later stage. Diagnosis of such degenerative diseases warrants early diagnosis and treatment. In this work, performance of statistical and textural features in retinal vessel segmentation is evaluated through classifiers like extreme learning machine, support vector machine and Random Forest. The fundus images are initially preprocessed for any noise reduction, image enhancement and contrast adjustment. The two-dimensional Gabor Wavelets and Partition Clustering is employed on the preprocessed image to extract the blood vessels. Finally, the combined hybrid features comprising statistical textural, intensity and vessel morphological features, extracted from the image, are used to detect glaucomatous abnormality through the classifiers. A crisp decision can be taken depending on the classifying rates of the classifiers. Public databases RIM-ONE and high-resolution fundus and local datasets are used for evaluation with threefold cross validation. The evaluation is based on performance metrics through accuracy, sensitivity and specificity. The evaluation of hybrid features obtained an overall accuracy of 97% when tested using classifiers. The support vector machine classifier is able to achieve an accuracy of 93.33% on high-resolution fundus, 93.8% on RIM-ONE dataset and 95.3% on local dataset. For extreme learning machine classifier, the accuracy is 95.1% on high-resolution fundus, 97.8% on RIM-ONE and 96.8% on local dataset. An accuracy of 94.5% on high-resolution fundus 92.5% on RIM-ONE and 94.2% on local dataset is obtained for the random forest classifier. Validation of the experiment results indicate that the hybrid features can be deployed in supervised classifiers to discriminate retinal abnormalities effectively.
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Michau, Gabriel, Yang Hu, Thomas Palmé, and Olga Fink. "Feature learning for fault detection in high-dimensional condition monitoring signals." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 234, no. 1 (August 24, 2019): 104–15. http://dx.doi.org/10.1177/1748006x19868335.

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Complex industrial systems are continuously monitored by a large number of heterogeneous sensors. The diversity of their operating conditions and the possible fault types make it impossible to collect enough data for learning all the possible fault patterns. This article proposes an integrated automatic unsupervised feature learning and one-class classification for fault detection that uses data on healthy conditions only for its training. The approach is based on stacked extreme learning machines (namely hierarchical extreme learning machines) and comprises an autoencoder, performing unsupervised feature learning, stacked with a one-class classifier monitoring the distance of the test data to the training healthy class, thereby assessing the health of the system. This study provides a comprehensive evaluation of hierarchical extreme learning machines fault detection capability compared to other machine learning approaches, such as stand-alone one-class classifiers (extreme learning machines and support vector machines); these same one-class classifiers combined with traditional dimensionality reduction methods (principal component analysis) and a deep belief network. The performance is first evaluated on a synthetic dataset that encompasses typical characteristics of condition monitoring data. Subsequently, the approach is evaluated on a real case study of a power plant fault. The proposed algorithm for fault detection, combining feature learning with the one-class classifier, demonstrates a better performance, particularly in cases where condition monitoring data contain several non-informative signals.
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Raza, Ali, Furqan Rustam, Hafeez Ur Rehman Siddiqui, Isabel de la Torre Diez, Begoña Garcia-Zapirain, Ernesto Lee, and Imran Ashraf. "Predicting Genetic Disorder and Types of Disorder Using Chain Classifier Approach." Genes 14, no. 1 (December 26, 2022): 71. http://dx.doi.org/10.3390/genes14010071.

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Genetic disorders are the result of mutation in the deoxyribonucleic acid (DNA) sequence which can be developed or inherited from parents. Such mutations may lead to fatal diseases such as Alzheimer’s, cancer, Hemochromatosis, etc. Recently, the use of artificial intelligence-based methods has shown superb success in the prediction and prognosis of different diseases. The potential of such methods can be utilized to predict genetic disorders at an early stage using the genome data for timely treatment. This study focuses on the multi-label multi-class problem and makes two major contributions to genetic disorder prediction. A novel feature engineering approach is proposed where the class probabilities from an extra tree (ET) and random forest (RF) are joined to make a feature set for model training. Secondly, the study utilizes the classifier chain approach where multiple classifiers are joined in a chain and the predictions from all the preceding classifiers are used by the conceding classifiers to make the final prediction. Because of the multi-label multi-class data, macro accuracy, Hamming loss, and α-evaluation score are used to evaluate the performance. Results suggest that extreme gradient boosting (XGB) produces the best scores with a 92% α-evaluation score and a 84% macro accuracy score. The performance of XGB is much better than state-of-the-art approaches, in terms of both performance and computational complexity.
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Afolabi, Hassan A., and Aburas A. Abdurazzag. "Statistical performance assessment of supervised machine learning algorithms for intrusion detection system." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (March 1, 2024): 266. http://dx.doi.org/10.11591/ijai.v13.i1.pp266-277.

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<span lang="EN-US">Several studies have shown that an ensemble classifier's effectiveness is directly correlated with the diversity of its members. However, the algorithms used to build the base learners are one of the issues encountered when using a stacking ensemble. Given the number of options, choosing the best ones might be challenging. In this study, we selected some of the most extensively applied supervised machine learning algorithms and performed a performance evaluation in terms of well-known metrics and validation methods using two internet of things (IoT) intrusion detection datasets, namely network-based anomaly internet of things (N-BaIoT) and internet of things intrusion detection dataset (IoTID20). Friedman and Dunn's tests are used to statistically examine the significant differences between the classifier groups. The goal of this study is to encourage security researchers to develop an intrusion detection system (IDS) using ensemble learning and to propose an appropriate method for selecting diverse base classifiers for a stacking-type ensemble. The performance results indicate that adaptive boosting, and gradient boosting (GB), gradient boosting machines (GBM), light gradient boosting machines (LGBM), extreme gradient boosting (XGB) and deep neural network (DNN) classifiers exhibit better trade-off between the performance parameters and classification time making them ideal choices for developing anomaly-based IDSs.</span>
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Thiamchoo, Nantarika, and Pornchai Phukpattaranont. "Evaluation of feature projection techniques in object grasp classification using electromyogram signals from different limb positions." PeerJ Computer Science 8 (May 6, 2022): e949. http://dx.doi.org/10.7717/peerj-cs.949.

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A myoelectric prosthesis is manipulated using electromyogram (EMG) signals from the existing muscles for performing the activities of daily living. A feature vector that is formed by concatenating data from many EMG channels may result in a high dimensional space, which may cause prolonged computation time, redundancy, and irrelevant information. We evaluated feature projection techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and spectral regression extreme learning machine (SRELM), applied to object grasp classification. These represent feature projections that are combinations of either linear or nonlinear, and supervised or unsupervised types. All pairs of the four types of feature projection with seven types of classifiers were evaluated, with data from six EMG channels and an IMU sensors for nine upper limb positions in the transverse plane. The results showed that SRELM outperformed LDA with supervised feature projections, and t-SNE was superior to PCA with unsupervised feature projections. The classification errors from SRELM and t-SNE paired with the seven classifiers were from 1.50% to 2.65% and from 1.27% to 17.15%, respectively. A one-way ANOVA test revealed no statistically significant difference by classifier type when using the SRELM projection, which is a nonlinear supervised feature projection (p = 0.334). On the other hand, we have to carefully select an appropriate classifier for use with t-SNE, which is a nonlinear unsupervised feature projection. We achieved the lowest classification error 1.27% using t-SNE paired with a k-nearest neighbors classifier. For SRELM, the lowest 1.50% classification error was obtained when paired with a neural network classifier.
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Kamaruddin, Ami Shamril, Mohd Fikri Hadrawi, Yap Bee Wah, and Sharifah Aliman. "An evaluation of nature-inspired optimization algorithms and machine learning classifiers for electricity fraud prediction." Indonesian Journal of Electrical Engineering and Computer Science 32, no. 1 (October 1, 2023): 468. http://dx.doi.org/10.11591/ijeecs.v32.i1.pp468-477.

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<span>This study evaluated the nature-inspired optimization algorithms to improve classification involving imbalanced class problems. The particle swarm optimization (PSO) and grey wolf optimizer (GWO) were used to adaptively balance the distribution and then four supervised machine learning classifiers artificial neural network (ANN), support vector machine (SVM), extreme gradient-boosted tree (XGBoost), and random forest (RF) were applied to maximize the classification performance for electricity fraud prediction. The imbalance data was balanced using random undersampling (RUS) and two nature-inspired algorithm techniques (PSO and GWO). Results showed that for the data balanced using random undersampling, ANN (Sentest = 50.31%), and XGBoost (Sentest = 66.32%) has better sensitivity than SVM (Sentest = 23.61%), while RF exhibits overfitting (Sentrain = 100%, Sentest = 71.25%). The classification performance of RF model hybrid with PSO improved tremendously (AccTest = 96.98%, Sentest = 94.87%, Spectest = 99.16%, Pretest = 99.14%, F1 Score = 96.96%, and area under the curve (AUC) = 0.989). This was closely followed by hybrid of XGBoost with PSO. Moreover, RF and XGBoost hybrid with GWO also showed an improvement and promising results. This study has showed that nature-inspired optimization algorithms (PSO and GWO) are effective methods in addressing imbalanced dataset.</span>
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Tian, Zhang, Chen, Geng, and Wang. "Selective Ensemble Based on Extreme Learning Machine for Sensor-Based Human Activity Recognition." Sensors 19, no. 16 (August 8, 2019): 3468. http://dx.doi.org/10.3390/s19163468.

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Sensor-based human activity recognition (HAR) has attracted interest both in academic and applied fields, and can be utilized in health-related areas, fitness, sports training, etc. With a view to improving the performance of sensor-based HAR and optimizing the generalizability and diversity of the base classifier of the ensemble system, a novel HAR approach (pairwise diversity measure and glowworm swarm optimization-based selective ensemble learning, DMGSOSEN) that utilizes ensemble learning with differentiated extreme learning machines (ELMs) is proposed in this paper. Firstly, the bootstrap sampling method is utilized to independently train multiple base ELMs which make up the initial base classifier pool. Secondly, the initial pool is pre-pruned by calculating the pairwise diversity measure of each base ELM, which can eliminate similar base ELMs and enhance the performance of HAR system by balancing diversity and accuracy. Then, glowworm swarm optimization (GSO) is utilized to search for the optimal sub-ensemble from the base ELMs after pre-pruning. Finally, majority voting is utilized to combine the results of the selected base ELMs. For the evaluation of our proposed method, we collected a dataset from different locations on the body, including chest, waist, left wrist, left ankle and right arm. The experimental results show that, compared with traditional ensemble algorithms such as Bagging, Adaboost, and other state-of-the-art pruning algorithms, the proposed approach is able to achieve better performance (96.7% accuracy and F1 from wrist) with fewer base classifiers.
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Guo, Weian, Yan Zhang, Ming Chen, Lei Wang, and Qidi Wu. "Fuzzy performance evaluation of Evolutionary Algorithms based on extreme learning classifier." Neurocomputing 175 (January 2016): 371–82. http://dx.doi.org/10.1016/j.neucom.2015.10.069.

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Al-Gethami, Khalid M., Mousa T. Al-Akhras, and Mohammed Alawairdhi. "Empirical Evaluation of Noise Influence on Supervised Machine Learning Algorithms Using Intrusion Detection Datasets." Security and Communication Networks 2021 (January 15, 2021): 1–28. http://dx.doi.org/10.1155/2021/8836057.

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Optimizing the detection of intrusions is becoming more crucial due to the continuously rising rates and ferocity of cyber threats and attacks. One of the popular methods to optimize the accuracy of intrusion detection systems (IDSs) is by employing machine learning (ML) techniques. However, there are many factors that affect the accuracy of the ML-based IDSs. One of these factors is noise, which can be in the form of mislabelled instances, outliers, or extreme values. Determining the extent effect of noise helps to design and build more robust ML-based IDSs. This paper empirically examines the extent effect of noise on the accuracy of the ML-based IDSs by conducting a wide set of different experiments. The used ML algorithms are decision tree (DT), random forest (RF), support vector machine (SVM), artificial neural networks (ANNs), and Naïve Bayes (NB). In addition, the experiments are conducted on two widely used intrusion datasets, which are NSL-KDD and UNSW-NB15. Moreover, the paper also investigates the use of these ML algorithms as base classifiers with two ensembles of classifiers learning methods, which are bagging and boosting. The detailed results and findings are illustrated and discussed in this paper.
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Okwonu, Friday Zinzendoff, Nor Aishah Ahad, Nicholas Oluwole Ogini, Innocent Ejiro Okoloko, and Wan Zakiyatussariroh Wan Husin. "COMPARATIVE PERFORMANCE EVALUATION OF EFFICIENCY FOR HIGH DIMENSIONAL CLASSIFICATION METHODS." Journal of Information and Communication Technology 21, No.3 (July 17, 2022): 437–64. http://dx.doi.org/10.32890/jict2022.21.3.6.

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This paper aimed to determine the efficiency of classifiers for high-dimensional classification methods. It also investigated whether an extreme minimum misclassification rate translates into robust efficiency. To ensure an acceptable procedure, a benchmark evaluation threshold (BETH) was proposed as a metric to analyze the comparative performance for high-dimensional classification methods. A simplified performance metric was derived to show the efficiency of different classification methods. To achieve the objectives, the existing probability of correct classification (PCC) or classification accuracy reported in five different articles was used to generate the BETH value. Then, a comparative analysis was performed between the application of BETH value and the well-established PCC value ,derived from the confusion matrix. The analysis indicated that the BETH procedure had a minimum misclassification rate, unlike the Optimal method. The results also revealed that as the PCC inclined toward unity value, the misclassification rate between the two methods (BETH and PCC) became extremely irrelevant. The study revealed that the BETH method was invariant to the performance established by the classifiers using the PCC criterion but demonstrated more relevant aspects of robustness and minimum misclassification rate as compared to the PCC method. In addition, the comparative analysis affirmed that the BETH method exhibited more robust efficiency than the Optimal method. The study concluded that a minimum misclassification rate yields robust performance efficiency.
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Dissertations / Theses on the topic "Evaluation of extreme classifiers"

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Legrand, Juliette. "Simulation and assessment of multivariate extreme models for environmental data." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASJ015.

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L'estimation précise des probabilités d'occurrence des événements extrêmes environnementaux est une préoccupation majeure dans l'évaluation des risques. Pour l'ingénierie côtière par exemple, le dimensionnement de structures implantées sur ou à proximité des côtes doit être tel qu'elles résistent aux événements les plus sévères qu'elles puissent rencontrer au cours de leur vie. Cette thèse porte sur la simulation d'événements extrêmes multivariés, motivée par des applications aux hauteurs significatives de vagues, et sur l'évaluation de modèles de prédiction d'occurrence d'événements extrêmes.Dans la première partie du manuscrit, nous proposons et étudions un simulateur stochastique qui génère conjointement, en fonction de certaines conditions d'état de mer au large, des extrêmes de hauteur significative de vagues (Hs) au large et à la côte. Pour cela, nous nous appuyons sur l'approche par dépassements de seuils bivariés et nous développons un algorithme de simulation non-paramétrique de lois de Pareto généralisées bivariées. À partir de ce simulateur d'événements cooccurrents, nous dérivons un modèle de simulation conditionnel. Les deux algorithmes de simulation sont mis en oeuvre sur des expériences numériques et appliqués aux extrêmes de Hs près des côtes bretonnes françaises. Un autre développement est traité quant à la modélisation des lois marginales des Hs. Afin de prendre en compte leur non-stationnaritée, nous adaptons une extension de la loi de Pareto généralisée, en considérant l'effet de la période et de la direction pic sur ses paramètres.La deuxième partie de cette thèse apporte un développement plus théorique. Pour évaluer différents modèles de prédiction d'extrêmes, nous étudions le cas spécifique des classifieurs binaires, qui constituent la forme la plus simple de prévision et de processus décisionnel : un événement extrême s'est produit ou ne s'est pas produit. Des fonctions de risque adaptées à la classification binaire d'événements extrêmes sont développées, ce qui nous permet de répondre à notre deuxième question. Leurs propriétés sont établies dans le cadre de la variation régulière multivariée et de la variation régulière cachée, permettant de considérer des formes plus fines d'indépendance asymptotique. Ces développements sont ensuite appliqués aux débits de rivière extrêmes
Accurate estimation of the occurrence probabilities of extreme environmental events is a major issue for risk assessment. For example, in coastal engineering, the design of structures installed at or near the coasts must be such that they can withstand the most severe events they may encounter in their lifetime. This thesis focuses on the simulation of multivariate extremes, motivated by applications to significant wave height, and on the evaluation of models predicting the occurrences of extreme events.In the first part of the manuscript, we propose and study a stochastic simulator that, given offshore conditions, produces jointly offshore and coastal extreme significant wave heights (Hs). We rely on bivariate Peaks over Threshold and develop a non-parametric simulation scheme of bivariate generalised Pareto distributions. From such joint simulator, we derive a conditional simulation model. Both simulation algorithms are applied to numerical experiments and to extreme Hs near the French Brittanny coast. A further development is addressed regarding the marginal modelling of Hs. To take into account non-stationarities, we adapt the extended generalised Pareto model, letting the marginal parameters vary with the peak period and the peak direction.The second part of this thesis provides a more theoretical development. To evaluate different prediction models for extremes, we study the specific case of binary classifiers, which are the simplest type of forecasting and decision-making situation: an extreme event did or did not occur. Risk functions adapted to binary classifiers of extreme events are developed, answering our second question. Their properties are derived under the framework of multivariate regular variation and hidden regular variation, allowing to handle finer types of asymptotic independence. This framework is applied to extreme river discharges
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Lavesson, Niklas. "Evaluation and Analysis of Supervised Learning Algorithms and Classifiers." Licentiate thesis, Karlskrona : Blekinge Institute of Technology, 2006. http://www.bth.se/fou/Forskinfo.nsf/allfirst2/c655a0b1f9f88d16c125714c00355e5d?OpenDocument.

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Nygren, Rasmus. "Evaluation of hyperparameter optimization methods for Random Forest classifiers." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301739.

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In order to create a machine learning model, one is often tasked with selecting certain hyperparameters which configure the behavior of the model. The performance of the model can vary greatly depending on how these hyperparameters are selected, thus making it relevant to investigate the effects of hyperparameter optimization on the classification accuracy of a machine learning model. In this study, we train and evaluate a Random Forest classifier whose hyperparameters are set to default values and compare its classification accuracy to another classifier whose hyperparameters are obtained through the use of the hyperparameter optimization (HPO) methods Random Search, Bayesian Optimization and Particle Swarm Optimization. This is done on three different datasets, and each HPO method is evaluated based on the classification accuracy change it induces across the datasets. We found that every HPO method yielded a total classification accuracy increase of approximately 2-3% across all datasets compared to the accuracies obtained using the default hyperparameters. However, due to limitations of time, data and computational resources, no assertions can be made as to whether the observed positive effect is generalizable at a larger scale. Instead, we could conclude that the utility of HPO methods is dependent on the dataset at hand.
För att skapa en maskininlärningsmodell behöver en ofta välja olika hyperparametrar som konfigurerar modellens egenskaper. Prestandan av en sådan modell beror starkt på valet av dessa hyperparametrar, varför det är relevant att undersöka hur optimering av hyperparametrar kan påverka klassifikationssäkerheten av en maskininlärningsmodell. I denna studie tränar och utvärderar vi en Random Forest-klassificerare vars hyperparametrar sätts till särskilda standardvärden och jämför denna med en klassificerare vars hyperparametrar bestäms av tre olika metoder för optimering av hyperparametrar (HPO) - Random Search, Bayesian Optimization och Particle Swarm Optimization. Detta görs på tre olika dataset, och varje HPO- metod utvärderas baserat på den ändring av klassificeringsträffsäkerhet som den medför över dessa dataset. Vi fann att varje HPO-metod resulterade i en total ökning av klassificeringsträffsäkerhet på cirka 2-3% över alla dataset jämfört med den träffsäkerhet som kruleslassificeraren fick med standardvärdena för hyperparametrana. På grund av begränsningar i form av tid och data kunde vi inte fastställa om den positiva effekten är generaliserbar till en större skala. Slutsatsen som kunde dras var istället att användbarheten av metoder för optimering av hyperparametrar är beroende på det dataset de tillämpas på.
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Dang, Robin, and Anders Nilsson. "Evaluation of Machine Learning classifiers for Breast Cancer Classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280349.

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Breast cancer is a common and fatal disease among women globally, where early detection is vital to improve the prognosis of patients. In today’s digital society, computers and complex algorithms can evaluate and diagnose diseases more efficiently and with greater certainty than experienced doctors. Several studies have been conducted to automate medical imaging techniques, by utilizing machine learning techniques, to predict and detect breast cancer. In this report, the suitability of using machine learning to classify whether breast cancer is of benign or malignant characteristic is evaluated. More specifically, five different machine learning methods are examined and compared. Furthermore, we investigate how the efficiency of the methods, with regards to classification accuracy and execution time, is affected by the preprocessing method Principal component analysis and the ensemble method Bootstrap aggregating. In theory, both methods should favor certain machine learning methods and consequently increase the classification accuracy. The study is based on a well-known breast cancer dataset from Wisconsin which is used to train the algorithms. The result was evaluated by applying statistical methods concerning the classification accuracy, sensitivity and execution time. Consequently, the results are then compared between the different classifiers. The study showed that the use of neither Principal component analysis nor Bootstrap aggregating resulted in any significant improvements in classification accuracy. However, the results showed that the support vector machines classifiers were the better performer. As the survey was limited in terms of the amount of datasets and the choice of different evaluation methods with associating adjustments, it is uncertain whether the obtained result can be generalized over other datasets or populations.
Bröstcancer är en vanlig och dödlig sjukdom bland kvinnor globalt där en tidig upptäckt är avgörande för att förbättra prognosen för patienter. I dagens digitala samhälle kan datorer och komplexa algoritmer utvärdera och diagnostisera sjukdomar mer effektivt och med större säkerhet än erfarna läkare. Flera studier har genomförts för att automatisera tekniker med medicinska avbildningsmetoder, genom maskininlärnings tekniker, för att förutsäga och upptäcka bröstcancer. I den här rapport utvärderas och jämförs lämpligheten hos fem olika maskininlärningsmetoder att klassificera huruvida bröstcancer är av god- eller elakartad karaktär. Vidare undersöks hur metodernas effektivitet, med avseende på klassificeringssäkerhet samt exekveringstid, påverkas av förbehandlingsmetoden Principal component analysis samt ensemble metoden Bootstrap aggregating. I teorin skall båda förbehandlingsmetoder gynna vissa maskininlärningsmetoder och således öka klassificeringssäkerheten. Undersökningen är baserat på ett välkänt bröstcancer dataset från Wisconsin som används till att träna algoritmerna. Resultaten är evaluerade genom applicering av statistiska metoder där träffsäkerhet, känslighet och exekveringstid tagits till hänsyn. Följaktligen jämförs resultaten mellan de olika klassificerarna. Undersökningen visade att användningen av varken Principal component analysis eller Bootstrap aggregating resulterade i några nämnvärda förbättringar med avseende på klassificeringssäkerhet. Dock visade resultaten att klassificerarna Support vector machines Linear och RBF presterade bäst. I och med att undersökningen var begränsad med avseende på antalet dataset samt val av olika evalueringsmetoder med medförande justeringar är det därför osäkert huruvida det erhållna resultatet kan generaliseras över andra dataset och populationer.
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Fischer, Manfred M., Sucharita Gopal, Petra Staufer-Steinnocher, and Klaus Steinocher. "Evaluation of Neural Pattern Classifiers for a Remote Sensing Application." WU Vienna University of Economics and Business, 1995. http://epub.wu.ac.at/4184/1/WSG_DP_4695.pdf.

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This paper evaluates the classification accuracy of three neural network classifiers on a satellite image-based pattern classification problem. The neural network classifiers used include two types of the Multi-Layer-Perceptron (MLP) and the Radial Basis Function Network. A normal (conventional) classifier is used as a benchmark to evaluate the performance of neural network classifiers. The satellite image consists of 2,460 pixels selected from a section (270 x 360) of a Landsat-5 TM scene from the city of Vienna and its northern surroundings. In addition to evaluation of classification accuracy, the neural classifiers are analysed for generalization capability and stability of results. Best overall results (in terms of accuracy and convergence time) are provided by the MLP-1 classifier with weight elimination. It has a small number of parameters and requires no problem-specific system of initial weight values. Its in-sample classification error is 7.87% and its out-of-sample classification error is 10.24% for the problem at hand. Four classes of simulations serve to illustrate the properties of the classifier in general and the stability of the result with respect to control parameters, and on the training time, the gradient descent control term, initial parameter conditions, and different training and testing sets. (authors' abstract)
Series: Discussion Papers of the Institute for Economic Geography and GIScience
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Alorf, Abdulaziz Abdullah. "Primary/Soft Biometrics: Performance Evaluation and Novel Real-Time Classifiers." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/96942.

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The relevance of faces in our daily lives is indisputable. We learn to recognize faces as newborns, and faces play a major role in interpersonal communication. The spectrum of computer vision research about face analysis includes, but is not limited to, face detection and facial attribute classification, which are the focus of this dissertation. The face is a primary biometric because by itself revels the subject's identity, while facial attributes (such as hair color and eye state) are soft biometrics because by themselves they do not reveal the subject's identity. In this dissertation, we proposed a real-time model for classifying 40 facial attributes, which preprocesses faces and then extracts 7 types of classical and deep features. These features were fused together to train 3 different classifiers. Our proposed model yielded 91.93% on the average accuracy outperforming 7 state-of-the-art models. We also developed a real-time model for classifying the states of human eyes and mouth (open/closed), and the presence/absence of eyeglasses in the wild. Our method begins by preprocessing a face by cropping the regions of interest (ROIs), and then describing them using RootSIFT features. These features were used to train a nonlinear support vector machine for each attribute. Our eye-state classifier achieved the top performance, while our mouth-state and glasses classifiers were tied as the top performers with deep learning classifiers. We also introduced a new facial attribute related to Middle Eastern headwear (called igal) along with its detector. Our proposed idea was to detect the igal using a linear multiscale SVM classifier with a HOG descriptor. Thereafter, false positives were discarded using dense SIFT filtering, bag-of-visual-words decomposition, and nonlinear SVM classification. Due to the similarity in real-life applications, we compared the igal detector with state-of-the-art face detectors, where the igal detector significantly outperformed the face detectors with the lowest false positives. We also fused the igal detector with a face detector to improve the detection performance. Face detection is the first process in any facial attribute classification pipeline. As a result, we reported a novel study that evaluates the robustness of current face detectors based on: (1) diffraction blur, (2) image scale, and (3) the IoU classification threshold. This study would enable users to pick the robust face detector for their intended applications.
Doctor of Philosophy
The relevance of faces in our daily lives is indisputable. We learn to recognize faces as newborns, and faces play a major role in interpersonal communication. Faces probably represent the most accurate biometric trait in our daily interactions. Thereby, it is not singular that so much effort from computer vision researchers have been invested in the analysis of faces. The automatic detection and analysis of faces within images has therefore received much attention in recent years. The spectrum of computer vision research about face analysis includes, but is not limited to, face detection and facial attribute classification, which are the focus of this dissertation. The face is a primary biometric because by itself revels the subject's identity, while facial attributes (such as hair color and eye state) are soft biometrics because by themselves they do not reveal the subject's identity. Soft biometrics have many uses in the field of biometrics such as (1) they can be utilized in a fusion framework to strengthen the performance of a primary biometric system. For example, fusing a face with voice accent information can boost the performance of the face recognition. (2) They also can be used to create qualitative descriptions about a person, such as being an "old bald male wearing a necktie and eyeglasses." Face detection and facial attribute classification are not easy problems because of many factors, such as image orientation, pose variation, clutter, facial expressions, occlusion, and illumination, among others. In this dissertation, we introduced novel techniques to classify more than 40 facial attributes in real-time. Our techniques followed the general facial attribute classification pipeline, which begins by detecting a face and ends by classifying facial attributes. We also introduced a new facial attribute related to Middle Eastern headwear along with its detector. The new facial attribute were fused with a face detector to improve the detection performance. In addition, we proposed a new method to evaluate the robustness of face detection, which is the first process in the facial attribute classification pipeline. Detecting the states of human facial attributes in real time is highly desired by many applications. For example, the real-time detection of a driver's eye state (open/closed) can prevent severe accidents. These systems are usually called driver drowsiness detection systems. For classifying 40 facial attributes, we proposed a real-time model that preprocesses faces by localizing facial landmarks to normalize faces, and then crop them based on the intended attribute. The face was cropped only if the intended attribute is inside the face region. After that, 7 types of classical and deep features were extracted from the preprocessed faces. Lastly, these 7 types of feature sets were fused together to train three different classifiers. Our proposed model yielded 91.93% on the average accuracy outperforming 7 state-of-the-art models. It also achieved state-of-the-art performance in classifying 14 out of 40 attributes. We also developed a real-time model that classifies the states of three human facial attributes: (1) eyes (open/closed), (2) mouth (open/closed), and (3) eyeglasses (present/absent). Our proposed method consisted of six main steps: (1) In the beginning, we detected the human face. (2) Then we extracted the facial landmarks. (3) Thereafter, we normalized the face, based on the eye location, to the full frontal view. (4) We then extracted the regions of interest (i.e., the regions of the mouth, left eye, right eye, and eyeglasses). (5) We extracted low-level features from each region and then described them. (6) Finally, we learned a binary classifier for each attribute to classify it using the extracted features. Our developed model achieved 30 FPS with a CPU-only implementation, and our eye-state classifier achieved the top performance, while our mouth-state and glasses classifiers were tied as the top performers with deep learning classifiers. We also introduced a new facial attribute related to Middle Eastern headwear along with its detector. After that, we fused it with a face detector to improve the detection performance. The traditional Middle Eastern headwear that men usually wear consists of two parts: (1) the shemagh or keffiyeh, which is a scarf that covers the head and usually has checkered and pure white patterns, and (2) the igal, which is a band or cord worn on top of the shemagh to hold it in place. The shemagh causes many unwanted effects on the face; for example, it usually occludes some parts of the face and adds dark shadows, especially near the eyes. These effects substantially degrade the performance of face detection. To improve the detection of people who wear the traditional Middle Eastern headwear, we developed a model that can be used as a head detector or combined with current face detectors to improve their performance. Our igal detector consists of two main steps: (1) learning a binary classifier to detect the igal and (2) refining the classier by removing false positives. Due to the similarity in real-life applications, we compared the igal detector with state-of-the-art face detectors, where the igal detector significantly outperformed the face detectors with the lowest false positives. We also fused the igal detector with a face detector to improve the detection performance. Face detection is the first process in any facial attribute classification pipeline. As a result, we reported a novel study that evaluates the robustness of current face detectors based on: (1) diffraction blur, (2) image scale, and (3) the IoU classification threshold. This study would enable users to pick the robust face detector for their intended applications. Biometric systems that use face detection suffer from huge performance fluctuation. For example, users of biometric surveillance systems that utilize face detection sometimes notice that state-of-the-art face detectors do not show good performance compared with outdated detectors. Although state-of-the-art face detectors are designed to work in the wild (i.e., no need to retrain, revalidate, and retest), they still heavily depend on the datasets they originally trained on. This condition in turn leads to variation in the detectors' performance when they are applied on a different dataset or environment. To overcome this problem, we developed a novel optics-based blur simulator that automatically introduces the diffraction blur at different image scales/magnifications. Then we evaluated different face detectors on the output images using different IoU thresholds. Users, in the beginning, choose their own values for these three settings and then run our model to produce the efficient face detector under the selected settings. That means our proposed model would enable users of biometric systems to pick the efficient face detector based on their system setup. Our results showed that sometimes outdated face detectors outperform state-of-the-art ones under certain settings and vice versa.
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Ayhan, Tezer Bahar. "Damage evaluation of civil engineering structures under extreme loadings." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2013. http://tel.archives-ouvertes.fr/tel-00975488.

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In many industrial and scientific domains, especially in civil engineering and mechanical engineering fields, materials that can be used on the microstructure scale, are highly heterogeneous by comparison to the nature of mechanical behavior. This feature can make the prediction of the behavior of the structure subjected to various loading types, necessary for sustainable design, difficult enough. The construction of civil engineering structures is regulated all over the world: the standards are more stringent and taken into account, up to a limit state, due to different loadings, for example severe loadings such as impact or earthquake. Behavior models of materials and structures must include the development of these design criteria and thereby become more complex, highly nonlinear. These models are often based on phenomenological approaches, are capable of reproducing the material response to the ultimate level. Stress-strain responses of materials under cyclic loading, for which many researches have been executed in the previous years in order to characterize and model, are defined by different kind of cyclic plasticity properties such as cyclic hardening, ratcheting and relaxation. By using the existing constitutive models, these mentioned responses can be simulated in a reasonable way. However, there may be failure in some simulation for the structural responses and local and global deformation. Inadequacy of these studies can be solved by developing strong constitutive models with the help of the experiments and the knowledge of the principles of working of different inelastic behavior mechanisms together. This dissertation develops a phenomenological constitutive model which is capable of coupling two basic inelastic behavior mechanisms, plasticity and damage by studying the cyclic inelastic features. In either plasticity or damage part, both isotropic and linear kinematic hardening effects are taken into account. The main advantage of the model is the use of independent plasticity versus damage criteria for describing the inelastic mechanisms. Another advantage concerns the numerical implementation of such model provided in hybrid-stress variational framework, resulting with much enhanced accuracy and efficient computation of stress and internal variables in each element. The model is assessed by simulating hysteresis loop shape, cyclic hardening, cyclic relaxation, and finally a series of ratcheting responses under uniaxial loading responses. Overall, this dissertation demonstrates a methodical and systematic development of a constitutive model for simulating a broad set of cycle responses. Several illustrative examples are presented in order to confirm the accuracy and efficiency of the proposed formulation in application to cyclic loading.
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Zuzáková, Barbora. "Exchange market pressure: an evaluation using extreme value theory." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-199589.

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This thesis discusses the phenomenon of currency crises, in particular it is devoted to empirical identification of crisis periods. As a crisis indicator, we aim to utilize an exchange market pressure index which has been revealed as a very powerful tool for the exchange market pressure quantification. Since enumeration of the exchange market pressure index is crucial for further analysis, we pay special attention to different approaches of its construction. In the majority of existing literature on exchange market pressure models, a currency crisis is defined as a period of time when the exchange market pressure index exceeds a predetermined level. In contrast to this, we incorporate a probabilistic approach using the extreme value theory. Our goal is to prove that stochastic methods are more accurate, in other words they are more reliable instruments for crisis identification. We illustrate the application of the proposed method on a selected sample of four central European countries over the period 1993 - 2012, or 1993 - 2008 respectively, namely the Czech Republic, Hungary, Poland and Slovakia. The choice of the sample is motivated by the fact that these countries underwent transition reforms to market economies at the beginning of 1990s and therefore could have been exposed to speculative attacks on their newly arisen currencies. These countries are often assumed to be relatively homogeneous group of countries at similar stage of the integration process. Thus, a resembling development of exchange market pressure, particularly during the last third of the estimation period, would not be surprising.
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Buolamwini, Joy Adowaa. "Gender shades : intersectional phenotypic and demographic evaluation of face datasets and gender classifiers." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/114068.

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Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 103-116).
This thesis (1) characterizes the gender and skin type distribution of IJB-A, a government facial recognition benchmark, and Adience, a gender classification benchmark, (2) outlines an approach for capturing images with more diverse skin types which is then applied to develop the Pilot Parliaments Benchmark (PPB), and (3) uses PPB to assess the classification accuracy of Adience, IBM, Microsoft, and Face++ gender classifiers with respect to gender, skin type, and the intersection of skin type and gender. The datasets evaluated are overwhelming lighter skinned: 79.6% - 86.24%. IJB-A includes only 24.6% female and 4.4% darker female, and features 59.4% lighter males. By construction, Adience achieves rough gender parity at 52.0% female but has only 13.76% darker skin. The Parliaments method for creating a more skin-type-balanced benchmark resulted in a dataset that is 44.39% female and 47% darker skin. An evaluation of four gender classifiers revealed a significant gap exists when comparing gender classification accuracies of females vs males (9 - 20%) and darker skin vs lighter skin (10 - 21%). Lighter males were in general the best classified group, and darker females were the worst classified group. 37% - 83% of classification errors resulted from the misclassification of darker females. Lighter males contributed the least to overall classification error (.4% - 3%). For the best performing classifier, darker females were 32 times more likely to be misclassified than lighter males. To increase the accuracy of these systems, more phenotypically diverse datasets need to be developed. Benchmark performance metrics need to be disaggregated not just by gender or skin type but by the intersection of gender and skin type. At a minimum, human-focused computer vision models should report accuracy on four subgroups: darker females, lighter females, darker males, and lighter males. The thesis concludes with a discussion of the implications of misclassification and the importance of building inclusive training sets and benchmarks.
by Joy Adowaa Buolamwini.
S.M.
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Pydipati, Rajesh. "Evaluation of classifiers for automatic disease detection in citrus leaves using machine vision." [Gainesville, Fla.] : University of Florida, 2004. http://purl.fcla.edu/fcla/etd/UFE0006991.

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Books on the topic "Evaluation of extreme classifiers"

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Margineantu, Dragos D. Bootstrap methods for the cost-sensitive evaluation of classifiers. [Corvallis, OR: Oregon State University, Dept. of Computer Science, 2000.

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Research, United States Office of Federal Coordinator for Meteorological Services and Supporting. Report on wind chill temperature and extreme heat indices: Evaluation and improvement projects. Washington, D.C: U.S. Department of Commerce, National Oceanic and Atmospheric Administration, Office of the Federal Coordinator for Meteorological Services and Supporting Research, 2003.

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Khan, S. M. Zubair Ali., Proshikhsan Shikhsa Kaj (Organization : Bangladesh), Proshikhsan Shikhsa Kaj (Organization : Bangladesh). Impact Monitoring and Evaluation Cell., and Great Britain. Dept. for International Development, Bangladesh., eds. Inclusion of the extreme poor to PROSHIKA activities. Dhaka: Impact Monitoring and Evaluation Cell, PROSHIKA, 2003.

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Matin, M. A. Risk assessment and evaluation of probability of extreme hydrological events: Case study from Noakhali Sadar and Subarnachar Upazilas. Dhaka: IUCN Bangladesh Country Office, 2008.

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Matin, M. A. Risk assessment and evaluation of probability of extreme hydrological events: Case study from Noakhali Sadar and Subarnachar Upazilas. Dhaka: IUCN Bangladesh Country Office, 2008.

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Daishinpuku jishindō to kenchikubutsu no taishinsei hyōka: Kyodai kaikōgata jishin, nairiku jishin ni sonaete = Extreme ground motions and seismic performance evaluation of buildings : how to prepare for mega subduction and inland earthquakes. Tōkyō-to Minato-ku: Nihon Kenchiku Gakkai, 2013.

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The Evaluation of Competing Classifiers. Storming Media, 2000.

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Meacham, Brian J. Extreme Event Mitigation in Buildings; Analysis and Design. National Fire Protection Association, 2006.

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Evaluation in the Extreme: Research, Impact and Politics in Violently Divided Societies. SAGE Publications India Pvt, Ltd., 2015.

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Bush, Kenneth, and Colleen Duggan. Evaluation in the Extreme: Research, Impact and Politics in Violently Divided Societies. SAGE Publications India Pvt, Ltd., 2015.

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Book chapters on the topic "Evaluation of extreme classifiers"

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Seewald, Alexander K., and Johannes Fürnkranz. "An Evaluation of Grading Classifiers." In Advances in Intelligent Data Analysis, 115–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44816-0_12.

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Alonzo, Todd A., and Margaret Sullivan Pepe. "Development and Evaluation of Classifiers." In Topics in Biostatistics, 89–116. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-530-5_6.

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Lóczy, Dénes. "Evaluation of Geomorphological Impact." In Geomorphological impacts of extreme weather, 363–70. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6301-2_23.

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Ashok, Pranav, Tomáš Brázdil, Krishnendu Chatterjee, Jan Křetínský, Christoph H. Lampert, and Viktor Toman. "Strategy Representation by Decision Trees with Linear Classifiers." In Quantitative Evaluation of Systems, 109–28. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30281-8_7.

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Torzilli, Guido, Guido Costa, Fabio Procopio, Luca Viganó, and Matteo Donadon. "Intraoperative Evaluation of Resectability." In Extreme Hepatic Surgery and Other Strategies, 177–93. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-13896-1_11.

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Szadkowski, Rudolf, Jan Drchal, and Jan Faigl. "Basic Evaluation Scenarios for Incrementally Trained Classifiers." In Lecture Notes in Computer Science, 507–17. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30484-3_41.

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Viechnicki, Peter. "A performance evaluation of automatic survey classifiers." In Grammatical Inference, 244–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0054080.

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Cieslak, Kasia P., Roelof J. Bennink, and Thomas M. van Gulik. "Preoperative Evaluation of Liver Function." In Extreme Hepatic Surgery and Other Strategies, 31–52. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-13896-1_3.

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Chao, J. Carlos Aguado. "Artificial Intelligence Classifiers and Their Social Impact." In Soft Computing for Risk Evaluation and Management, 170–94. Heidelberg: Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-7908-1814-7_11.

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Nirkhi, Smita. "Evaluation of Classifiers for Detection of Authorship Attribution." In Computational Intelligence: Theories, Applications and Future Directions - Volume I, 227–36. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1132-1_18.

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Conference papers on the topic "Evaluation of extreme classifiers"

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Britto, Larissa, and Luciano Pacífico. "Classificação de Espécies de Plantas Usando Extreme Learning Machine." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/eniac.2019.9268.

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Plants play an important role in nature, but correct plant species identification is still a challenging task for non-specialized people. Many works have been proposed towards the development of automatic plant species recognition systems through Machine Learning methods, but most of them lack the proper experimental analysis. In this work, we evaluate the performance of a general-purpose Artificial Neural Network to perform plant classification task: the Extreme Learning Machine (ELM).We compare ELM with several classifiers from plant recognition literature by means of three real-world data sets obtained from different image processing and feature extraction processes. A statistical hypothesis test is employed to perform proper experimental evaluation.
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Flores, Christian, Christian Fonseca, David Achanccaray, and Javier Andreu-Perez. "Performance Evaluation of a P300 Brain-Computer Interface Using a Kernel Extreme Learning Machine Classifier." In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2018. http://dx.doi.org/10.1109/smc.2018.00629.

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Itikawa, M. A., V. R. R. Ahón, T. A. Souza, A. M. V. Carrasco, J. C. Q. Neto, J. L. S. Gomes, R. R. H. Cavalcante, et al. "Automatic Cement Evaluation Using Machine Learning." In Offshore Technology Conference Brasil. OTC, 2023. http://dx.doi.org/10.4043/32961-ms.

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Abstract Cementing is an extremely important step in the well construction process. It has important objectives such as hydraulic sealing to prevent migration of undesired fluids from the formations and their collapse. One of the methods to verify the quality of cementat jobs is running acoustic logging tools such as CBL/VDL and ultrasonic and inferring zonal isolation by the interpretation of such data. This study aims to use machine learning techniques for automatic cement logs interpration. Cement logs of 25 wells were used as database. The logs responses have been classified in five classes according to the bond quality by specialized interpreters. These classified segments were used to train neural networks and other supervised machine learning models, such as random forests and k-nearest neighbor (KNN). Feature engineering is used in order to find new and high-performance features. The models were developed in a Jupyter environment using Python libraries. The best classifier has a simple accuracy of 61.4% and approximate accuracy (where the prediction is up to one class away from target) of 91.3%.
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Gautam, Chandan, Aruna Tiwari, and Sriram Ravindran. "Construction of multi-class classifiers by Extreme Learning Machine based one-class classifiers." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727445.

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Fein-Ashley, Jacob, Tian Ye, Rajgopal Kannan, Viktor Prasanna, and Carl Busart. "Benchmarking Deep Learning Classifiers for SAR Automatic Target Recognition." In 2023 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2023. http://dx.doi.org/10.1109/hpec58863.2023.10363455.

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Sivaguru, Raaghavi, Chhaya Choudhary, Bin Yu, Vadym Tymchenko, Anderson Nascimento, and Martine De Cock. "An Evaluation of DGA Classifiers." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8621875.

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Yusa, Mochammad, and Ema Utami. "Classifiers evaluation: Comparison of performance classifiers based on tuples amount." In 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI). IEEE, 2017. http://dx.doi.org/10.1109/eecsi.2017.8239204.

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Goldman, Alfredo, and Paulo Floriano. "An evaluation system." In the 3rd Extreme Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2414393.2414401.

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Assayed, Suha Khalil, Khaled Shaalan, Manar Alkhatib, and Safwan Maghaydah. "Machine Learning Chatbot for Sentiment Analysis of Covid-19 Tweets." In 10th International Conference on Computer Networks & Communications (CCNET 2023). Academy and Industry Research Collaboration Center (AIRCC), 2023. http://dx.doi.org/10.5121/csit.2023.130404.

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The various types of social media were increased rapidly, as people’s need to share knowledge between others. In fact, there are various types of social media apps and platforms such as Facebook, Twitter, Reddit, Instagram, and others. Twitter remains one of the most popular social application that people use for sharing their emotional states. However, this has increased particularly during the COVID-19 pandemic. In this paper, we proposed a chatbot for evaluating the sentiment analysis by using machine learning algorithms. The authors used a dataset of tweets from Kaggle’s website, and that includes 41157 tweets that are related to the COVID-19. These tweets were classified and labelled to four categories: Extremely positive, positive, neutral, negative, and extremely negative. In this study, we applied Machine Learning algorithms, Support Vector Machines (SVM), and the Naïve Bayes (NB) algorithms and accordingly, we compared the accuracy between them. In addition to that, the classifiers were evaluated and compared after changing the test split ratio. The result shows that the accuracy performance of SVM algorithm is better than Naïve Bayes algorithm, even though Naïve Bayes perform poorly with low accuracy, but it trained the data faster comparing to SVM.
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Yoo, Youngwoo, and Se-Young Oh. "Fast training of convolutional neural network classifiers through extreme learning machines." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727403.

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Reports on the topic "Evaluation of extreme classifiers"

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KAB LABS INC SAN DIEGO CA. Feature Set Evaluation for Classifiers. Fort Belvoir, VA: Defense Technical Information Center, March 1989. http://dx.doi.org/10.21236/ada226903.

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KAB LABS INC SAN DIEGO CA. Feature Set Evaluation for Classifiers. Fort Belvoir, VA: Defense Technical Information Center, January 1989. http://dx.doi.org/10.21236/ada226905.

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Liguori, Giovanni, and Nadia Pinardi. Evaluation of Extreme Forecast Indices (WP5+6). EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d4.11.

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While originally developed for weather forecasting, the Extreme Forecast index (EFI) concept has found utility in diverse fields. This study marks the inaugural application of EFI principles to numerical ocean forecasting. EFI offers a metric to gauge the forecast's deviation from historical norms specific to the location and time of year. A heightened EFI value signifies that the forecast falls beyond the usual range of variability, signifying a higher probability of extreme conditions. This novel use of EFI stands to benefit oceanographers by identifying significant oceanic events, aiding decision-making, and supporting early warning systems, particularly for extreme marine conditions. It enhances comprehension of forecast uncertainties and facilitates clearer communication of potential risks to the public and stakeholders. Such insights are invaluable for preparedness, coastal management, and mitigating the impact of marine extremes on communities and ecosystems. EFI indices for the Mediterranean Sea are computed using a first implementation of a forecast ensemble system that is being developed for the Mediterranean Sea Monitoring and Forecasting Center of the Copernicus Marine Environment Service. This deliverable report presents the first-ever application of the EFI approach to the Mediterranean Sea. After presenting the EFI definition adopted in this study, we discuss its application to sea surface temperature (SST) and sea surface height (SSH) extremes. A case studies using ensemble forecasts for the year 2021 are presented and discussed. (EuroSea Deliverable, D4.11)
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Asenath-Smith, Emily, Terry Melendy, Amelia Menke, Andrew Bernier, and George Blaisdell. Evaluation of airfield damage repair methods for extreme cold temperatures. Engineer Research and Development Center (U.S.), March 2019. http://dx.doi.org/10.21079/11681/32298.

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Ruby, Brent C. Evaluation of the Human/Extreme Environment Interaction: Implications for Enhancing Operational Performance and Recovery. Fort Belvoir, VA: Defense Technical Information Center, October 2011. http://dx.doi.org/10.21236/ada592672.

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Ruby, Brent C. Evaluation of the Human/Extreme Environment Interaction: Implications for Enhancing Operational Performance and Recovery. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada592673.

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Ruby, Brent C. Evaluation of the Human/Extreme Environment Interaction: Implications for Enhancing Operational Performance and Recovery. Fort Belvoir, VA: Defense Technical Information Center, February 2014. http://dx.doi.org/10.21236/ada600954.

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Bäumler, Maximilian, and Matthias Lehmann. Generating representative test scenarios: The FUSE for Representativity (fuse4rep) process model for collecting and analysing traffic observation data. TU Dresden, 2024. http://dx.doi.org/10.26128/2024.2.

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Scenario-based testing is a pillar of assessing the effectiveness of automated driving systems (ADSs). For data-driven scenario-based testing, representative traffic scenarios need to describe real road traffic situations in compressed form and, as such, cover normal driving along with critical and accident situations originating from different data sources. Nevertheless, in the choice of data sources, a conflict often arises between sample quality and depth of information. Police accident data (PD) covering accident situations, for example, represent a full survey and thus have high sample quality but low depth of information. However, for local video-based traffic observation (VO) data using drones and covering normal driving and critical situations, the opposite is true. Only the fusion of both sources of data using statistical matching can yield a representative, meaningful database able to generate representative test scenarios. For successful fusion, which requires as many relevant, shared features in both data sources as possible, the following question arises: How can VO data be collected by drones and analysed to create the maximum number of relevant, shared features with PD? To answer that question, we used the Find–Unify–Synthesise–Evaluation (FUSE) for Representativity (FUSE4Rep) process model.We applied the first (“Find”) and second (“Unify”) step of this model to VO data and conducted drone-based VOs at two intersections in Dresden, Germany, to verify our results. We observed a three-way and a four-way intersection, both without traffic signals, for more than 27 h, following a fixed sample plan. To generate as many relevant information as possible, the drone pilots collected 122 variables for each observation (which we published in the ListDB Codebook) and the behavioural errors of road users, among other information. Next, we analysed the videos for traffic conflicts, which we classified according to the German accident type catalogue and matched with complementary information collected by the drone pilots. Last, we assessed the crash risk for the detected traffic conflicts using generalised extreme value (GEV) modelling. For example, accident type 211 was predicted as happening 1.3 times per year at the observed four-way intersection. The process ultimately facilitated the preparation of VO data for fusion with PD. The orientation towards traffic conflicts, the matched behavioural errors and the estimated GEV allowed creating accident-relevant scenarios. Thus, the model applied to VO data marks an important step towards realising a representative test scenario database and, in turn, safe ADSs.
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Truffer-Moudra, Dana, Sarah Azmi-Wendler, Robbin Garber-Slaght, Prateek Shrestha, Qwerty Mackey, and Conor Dennehy. Performance Evaluation and Costs of a Combined Ground Source Heat Pump and Solar Photovoltaic Storage System in an Extreme Cold Climate. Office of Scientific and Technical Information (OSTI), June 2023. http://dx.doi.org/10.2172/1986504.

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10

Huntington, Dale. Anti-trafficking programs in South Asia: Appropriate activities, indicators and evaluation methodologies. Population Council, 2002. http://dx.doi.org/10.31899/rh2002.1019.

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Throughout South Asia, men, women, boys, and girls are trafficked within their own countries and across international borders against their wills in what is essentially a clandestine slave trade. The Congressional Research Service and the U.S. State Department estimate that between 1 to 2 million people are trafficked each year worldwide with the majority originating in Asia. Root causes include extreme disparities of wealth, increased awareness of job opportunities far from home, pervasive inequality due to caste, class, and gender bias, lack of transparency in regulations governing labor migration, poor enforcement of internationally agreed-upon human rights standards, and the enormous profitability for traffickers. The Population Council, UNIFEM, and PATH led a participatory approach to explore activities that address the problem of human trafficking in South Asia. A meeting was held in Kathmandu, Nepal, September 11– 13, 2001 to discuss these issues. Approximately 50 representatives from South Asian institutions, United Nations agencies, and international and local NGOs attended. This report summarizes the principal points from each paper presented and captures important discussion points that emerged from each panel presentation.
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