JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2022, Vol. 57 ›› Issue (4): 1-11.doi: 10.6040/j.issn.1671-9352.7.2021.167

   

Multilabel feature selection algorithm based on improved ReliefF

SUN Lin1,2, CHEN Yu-sheng1, XU Jiu-cheng1,2   

  1. 1. College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, Henan, China;
    2. Henan Engineering Laboratory of Smart Business and Internet of Things Technology, Xinxiang 453007, Henan, China
  • Published:2022-03-29

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Abstract: Aiming at the problems that the traditional ReliefF algorithm can only process single-label data, and its improved algorithms do not make full use of the correlation between samples, a multilabel feature selection algorithm based on improved ReliefF is proposed. First, the cosine similarity function is used to measure the similarity between features of samples, the Jaccard distance is employed to measure the correlation of labels among labels of samples, and then the similarity function among samples is defined to measure the similarity relationship between samples in the entire sample space. Second, the discrimination formula of the homogeneous or heterogeneous samples is defined to judge the nearest homogeneous or heterogeneous samples for the random samples. Finally, a new iterative formula of feature weights is proposed to improve the ReliefF algorithm, and then a multi-label feature selection algorithm is designed. The five different evaluation metrics including Average Precision, Coverage, One-error,Ranking Loss and Hamming Loss are employed to analyze and test the classification performance of the proposed algorithm on seven public multilabel datasets. The experimental results show that the proposed algorithm is effective.

Key words: multilabel, feature selection, correlation of labels, ReliefF

CLC Number: 

  • TP181
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