A direct measure of discriminant and characteristic capability for classifier building and assessment

Performance measures are used in various stages of the process aimed at solving a classification problem. Unfortunately, most of these measures are in fact biased, meaning that they strictly depend on the class ratio – i.e. on the imbalance between negative and positive samples. After pointing to th...

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Bibliographic Details
Published in:Information sciences 2015-12, Vol.325, p.466-483
Main Author: Armano, Giuliano
Format: Article
Language:English
Subjects:
Assessments
Classification
Classifier performance measures
Classifiers
Confusion matrices
Construction
Feature ranking/selection
Machine learning
Pattern recognition
Performance assessment
Tasks
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Summary:Performance measures are used in various stages of the process aimed at solving a classification problem. Unfortunately, most of these measures are in fact biased, meaning that they strictly depend on the class ratio – i.e. on the imbalance between negative and positive samples. After pointing to the source of bias for the best known measures, novel unbiased measures are defined which are able to capture the concepts of discriminant and characteristic capability. The combined use of these measures can give important information to researchers involved in machine learning or pattern recognition tasks, in particular for classifier performance assessment and feature selection.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2015.07.028