Understanding the classes better with class-specific and rule-specific feature selection, and redundancy control in a fuzzy rule based framework

TL;DR

This paper introduces a novel class-specific feature selection method within a fuzzy rule-based classifier that avoids common issues of existing methods, controls redundancy, and models substructures within classes, validated on synthetic datasets.

Contribution

The paper presents a new class-specific feature selection approach embedded in a fuzzy classifier, addressing drawbacks of traditional methods and enabling redundancy control and substructure modeling.

Findings

Effective feature selection for classes without one-vs-all split

Redundancy control improves feature subset quality

Validated on synthetic datasets showing promising results

Abstract

Recently, several studies have claimed that using class-specific feature subsets provides certain advantages over using a single feature subset for representing the data for a classification problem. Unlike traditional feature selection methods, the class-specific feature selection methods select an optimal feature subset for each class. Typically class-specific feature selection (CSFS) methods use one-versus-all split of the data set that leads to issues such as class imbalance, decision aggregation, and high computational overhead. We propose a class-specific feature selection method embedded in a fuzzy rule-based classifier, which is free from the drawbacks associated with most existing class-specific methods. Additionally, our method can be adapted to control the level of redundancy in the class-specific feature subsets by adding a suitable regularizer to the learning objective. Our method results in class-specific rules involving class-specific subsets. We also propose an extension where different rules of a particular class are defined by different feature subsets to model different substructures within the class. The effectiveness of the proposed method has been validated through experiments on three synthetic data sets.