Performance metrics are used in various stages of the process aimed at solving a classification problem. Unfortunately, most of these metrics 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 most acknowledged metrics, novel unbiased metrics are defined, able to capture the concepts of discriminant and characteristic capability. The combined use of these metrics can give important information to researchers involved in machine learning or pattern recognition tasks, such as classifier performance assessment and feature selection.

Measuring Discriminant and Characteristic Capability for Building and Assessing Classifiers

ARMANO, GIULIANO;GIULIANI, ALESSANDRO
2014-01-01

Abstract

Performance metrics are used in various stages of the process aimed at solving a classification problem. Unfortunately, most of these metrics 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 most acknowledged metrics, novel unbiased metrics are defined, able to capture the concepts of discriminant and characteristic capability. The combined use of these metrics can give important information to researchers involved in machine learning or pattern recognition tasks, such as classifier performance assessment and feature selection.
Stopwords identification; Discriminant Capability; Characteristic Capability
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/81291
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