Feature Selection with Redundancy-complementariness Dispersion

TL;DR

This paper introduces a novel feature selection method that accounts for feature complementariness and dispersion of redundancy, improving classification performance over existing methods.

Contribution

It proposes a modified evaluation criterion incorporating complementariness and dispersion, addressing limitations of pairwise correlation-based feature selection.

Findings

Outperforms five existing feature selection methods in classification tasks.

Demonstrates effectiveness across ten datasets with four classifiers.

Validates the importance of considering complementariness and dispersion in feature selection.

Abstract

Feature selection has attracted significant attention in data mining and machine learning in the past decades. Many existing feature selection methods eliminate redundancy by measuring pairwise inter-correlation of features, whereas the complementariness of features and higher inter-correlation among more than two features are ignored. In this study, a modification item concerning the complementariness of features is introduced in the evaluation criterion of features. Additionally, in order to identify the interference effect of already-selected False Positives (FPs), the redundancy-complementariness dispersion is also taken into account to adjust the measurement of pairwise inter-correlation of features. To illustrate the effectiveness of proposed method, classification experiments are applied with four frequently used classifiers on ten datasets. Classification results verify the superiority of proposed method compared with five representative feature selection methods.