A Note on Comparison of F-measures
Wei Ju, Wenxin Jiang
TL;DR
This paper discusses and improves methods for comparing F-measures in classification algorithms, especially on imbalanced datasets, by commenting on and enhancing a recent approach.
Contribution
It provides two specific improvements to the comparison of F-measures, refining the evaluation of classification performance.
Findings
Enhanced comparison method for F-measures
Improved accuracy in evaluating imbalanced data classifiers
Clarified limitations of previous approaches
Abstract
We comment on a recent TKDE paper "Linear Approximation of F-measure for the Performance Evaluation of Classification Algorithms on Imbalanced Data Sets", and make two improvements related to comparison of F-measures for two prediction rules.
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Taxonomy
TopicsImbalanced Data Classification Techniques · Rough Sets and Fuzzy Logic · Data Mining Algorithms and Applications