Multilabel Relief-Based Feature Selection via Fusing K-Means Clustering and Label Correlation
FENG Chanwu
SUN Lin
Abstract：Existing Relief algorithms are deficient in exploiting label correlation and often ignore the valua-hle information provided by local label correlation. To address this problem，this work proposed a multi-label Relief feature selection method that fuses K-means clustering with label correlation. First，to fully consider the relevance of sample labels，the K-means clustering algorithm was used to cluster the samples and divide them into different clusters，thereby constructing the local label space of the samples. Second, the Euclidean distance of all samples is defined to measure the global labeling correlation of the samples. At the same time, the traditional cosine similarity was improved by using the square root of the L1 norm for optimization, and this improved cosine similarity was applied in the local label space to efficiently ob-tain the local label correlation of the samples. Finally, on the basis of the Relief algorithm，the global la-bel correlation and local label correlation of the samples were fused，as the basis for measuring the similar-ity of the samples. Then，the nearest-neighbor similar samples and nearest-neighbor dissimilar samples were discriminated to finally obtain the feature weights. To verify the effectiveness of the proposed algo-rithm，comparison tests were conducted on 10 publicly available multilabel datasets，and the experimental results proved that the proposed algorithm shown significant advantages over other multilabel feature se-lection algorithms.
Keywords：multilabel learningfeature selection K-means clusteringlabel correlationRelief algorithm
Publication Date：2025-02-27
Online Publishing Date：2026-08-28(First online date of this platform, not the publication date of the document)
Pages：13( 122-134 )