Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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Statistical Comparisons of Classifiers over Multiple Data Sets
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A systematic analysis of performance measures for classification tasks
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Context-sensitive refinements for stochastic optimisation algorithms in inductive logic programming
Artificial Intelligence Review
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ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
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One-sided prototype selection on class imbalanced dissimilarity matrices
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Relevance as a metric for evaluating machine learning algorithms
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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Computer Methods and Programs in Biomedicine
Adjusted F-measure and kernel scaling for imbalanced data learning
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International Journal of Information Technology and Web Engineering
Robust classification of imbalanced data using one-class and two-class SVM-based multiclassifiers
Intelligent Data Analysis - Business Analytics and Intelligent Optimization
Alternative second-order cone programming formulations for support vector classification
Information Sciences: an International Journal
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Different evaluation measures assess different characteristics of machine learning algorithms. The empirical evaluation of algorithms and classifiers is a matter of on-going debate among researchers. Most measures in use today focus on a classifier's ability to identify classes correctly. We note other useful properties, such as failure avoidance or class discrimination, and we suggest measures to evaluate such properties. These measures – Youden's index, likelihood, Discriminant power – are used in medical diagnosis. We show that they are interrelated, and we apply them to a case study from the field of electronic negotiations. We also list other learning problems which may benefit from the application of these measures.