Feature Selection Based on Confidence Machine

TL;DR

This paper introduces a novel unsupervised feature selection method based on Confidence Machine, which estimates feature reliability to improve selection by balancing relevance and redundancy, showing promising results on benchmark datasets.

Contribution

The paper proposes a new filter-based unsupervised feature selection approach using Confidence Machine to evaluate feature reliability, enhancing selection accuracy.

Findings

Outperforms classic methods like Laplacian Score, Pearson Correlation, PCA

Demonstrates efficiency and effectiveness on benchmark datasets

Provides a mathematical model for Confidence Machine in feature selection

Abstract

In machine learning and pattern recognition, feature selection has been a hot topic in the literature. Unsupervised feature selection is challenging due to the loss of labels which would supply the related information.How to define an appropriate metric is the key for feature selection. We propose a filter method for unsupervised feature selection which is based on the Confidence Machine. Confidence Machine offers an estimation of confidence on a feature'reliability. In this paper, we provide the math model of Confidence Machine in the context of feature selection, which maximizes the relevance and minimizes the redundancy of the selected feature. We compare our method against classic feature selection methods Laplacian Score, Pearson Correlation and Principal Component Analysis on benchmark data sets. The experimental results demonstrate the efficiency and effectiveness of our method.