Feature selection using nearest attributes

TL;DR

This paper introduces a novel feature selection method that assesses features based on their discriminatory ability by analyzing the overlap of inter-class and intra-class distances, leading to improved classification performance on high-dimensional data.

Contribution

The proposed approach uniquely focuses on the discriminatory power of features using distance overlap, differing from traditional redundancy-based methods.

Findings

Achieved state-of-the-art recognition results on benchmark datasets.

Effective feature selection for high-dimensional image and microarray data.

Improved classification accuracy with the nearest neighbor classifier.

Abstract

Feature selection is an important problem in high-dimensional data analysis and classification. Conventional feature selection approaches focus on detecting the features based on a redundancy criterion using learning and feature searching schemes. In contrast, we present an approach that identifies the need to select features based on their discriminatory ability among classes. Area of overlap between inter-class and intra-class distances resulting from feature to feature comparison of an attribute is used as a measure of discriminatory ability of the feature. A set of nearest attributes in a pattern having the lowest area of overlap within a degree of tolerance defined by a selection threshold is selected to represent the best available discriminable features. State of the art recognition results are reported for pattern classification problems by using the proposed feature selection scheme with the nearest neighbour classifier. These results are reported with benchmark databases having high dimensional feature vectors in the problems involving images and micro array data.