Feature Selection for MAUC-Oriented Classification Systems

TL;DR

This paper introduces MDFS, a novel filter feature selection method tailored for multi-class classification systems aiming to maximize MAUC, demonstrating its superiority over existing methods through extensive experiments.

Contribution

The paper presents MDFS, the first feature selection method specifically designed to optimize MAUC in multi-class classification tasks.

Findings

MDFS outperforms several existing feature selection methods.

MDFS effectively improves MAUC in multi-class classification.

Empirical results validate the advantages of MDFS.

Abstract

Feature selection is an important pre-processing step for many pattern classification tasks. Traditionally, feature selection methods are designed to obtain a feature subset that can lead to high classification accuracy. However, classification accuracy has recently been shown to be an inappropriate performance metric of classification systems in many cases. Instead, the Area Under the receiver operating characteristic Curve (AUC) and its multi-class extension, MAUC, have been proved to be better alternatives. Hence, the target of classification system design is gradually shifting from seeking a system with the maximum classification accuracy to obtaining a system with the maximum AUC/MAUC. Previous investigations have shown that traditional feature selection methods need to be modified to cope with this new objective. These methods most often are restricted to binary classification problems only. In this study, a filter feature selection method, namely MAUC Decomposition based Feature Selection (MDFS), is proposed for multi-class classification problems. To the best of our knowledge, MDFS is the first method specifically designed to select features for building classification systems with maximum MAUC. Extensive empirical results demonstrate the advantage of MDFS over several compared feature selection methods.