A new framework for optimal classifier design

TL;DR

This paper introduces a novel classifier design framework that optimizes alternative performance measures like the F-measure, especially useful for imbalanced datasets, demonstrating robustness across multiple datasets.

Contribution

It presents a new algorithm for designing classifiers optimized for alternative measures, extending beyond traditional accuracy-focused methods.

Findings

The classifier effectively optimizes the F-measure.

The approach is robust across different datasets.

The method can be adapted to other evaluation metrics.

Abstract

The use of alternative measures to evaluate classifier performance is gaining attention, specially for imbalanced problems. However, the use of these measures in the classifier design process is still unsolved. In this work we propose a classifier designed specifically to optimize one of these alternative measures, namely, the so-called F-measure. Nevertheless, the technique is general, and it can be used to optimize other evaluation measures. An algorithm to train the novel classifier is proposed, and the numerical scheme is tested with several databases, showing the optimality and robustness of the presented classifier.