An Effective Feature Selection Method Based on Pair-Wise Feature Proximity for High Dimensional Low Sample Size Data

TL;DR

This paper introduces a novel feature selection method tailored for high-dimensional, low-sample-size data, focusing on pairwise sample proximity to improve selection efficacy where traditional methods falter.

Contribution

The proposed method evaluates feature relevance based on pairwise sample distances, addressing limitations of existing criteria in HDLSS scenarios, and demonstrates superior performance on benchmark datasets.

Findings

Outperforms existing feature selection methods on HDLSS data

Effective in low sample size scenarios

Enhances feature relevance assessment through pairwise analysis

Abstract

Feature selection has been studied widely in the literature. However, the efficacy of the selection criteria for low sample size applications is neglected in most cases. Most of the existing feature selection criteria are based on the sample similarity. However, the distance measures become insignificant for high dimensional low sample size (HDLSS) data. Moreover, the variance of a feature with a few samples is pointless unless it represents the data distribution efficiently. Instead of looking at the samples in groups, we evaluate their efficiency based on pairwise fashion. In our investigation, we noticed that considering a pair of samples at a time and selecting the features that bring them closer or put them far away is a better choice for feature selection. Experimental results on benchmark data sets demonstrate the effectiveness of the proposed method with low sample size, which outperforms many other state-of-the-art feature selection methods.