Unsupervised feature selection algorithm framework based on neighborhood interval disturbance fusion

TL;DR

This paper introduces a novel unsupervised feature selection framework called Neighborhood Interval Disturbance Fusion (NIDF), which improves stability and performance by combining data preprocessing with joint feature scoring and interval approximation.

Contribution

The paper proposes a new unsupervised feature selection algorithm that integrates data interval approximation with neighborhood disturbance fusion, enhancing stability and effectiveness.

Findings

NIDF outperforms existing methods in stability and accuracy.

The interval data set approximation improves feature selection robustness.

Experimental results verify the superiority of the proposed framework.

Abstract

Feature selection technology is a key technology of data dimensionality reduction. Becauseof the lack of label information of collected data samples, unsupervised feature selection has attracted more attention. The universality and stability of many unsupervised feature selection algorithms are very low and greatly affected by the dataset structure. For this reason, many researchers have been keen to improve the stability of the algorithm. This paper attempts to preprocess the data set and use an interval method to approximate the data set, experimentally verifying the advantages and disadvantages of the new interval data set. This paper deals with these data sets from the global perspective and proposes a new algorithm-unsupervised feature selection algorithm based on neighborhood interval disturbance fusion(NIDF). This method can realize the joint learning of the final score of the feature and the approximate data interval. By comparing with the original unsupervised feature selection methods and several existing feature selection frameworks, the superiority of the proposed model is verified.