|Table of Contents|

Weak Label Feature Selection Method Based on AP Clustering and Mutual Information(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2022年03期
Page:
108-115
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Weak Label Feature Selection Method Based on AP Clustering and Mutual Information
Author(s):
Sun Lin; Shi Enhui; Si Shanshan; Xu Jiucheng
(College of Computer and Information Engineering,Henan Normal University,Xinxiang 453007,China)
Keywords:
multi-label learning; feature selection; AP clustering; mutual information; missing labels
PACS:
TP399
DOI:
10.3969/j.issn.1001-4616.2022.03.014
Abstract:
Feature selection is an important preprocessing process in multi-label learning. To address the issues that some multi-label classification methods do not consider the influence of the proportion of label on the correlation between features and label sets and cannot efficiently deal with weak label data,a weak label feature selection method based on affinity propagation(AP)clustering and mutual information was proposed. Firstly,to effectively fill in all missing labels,the combination of the remaining label information with the similarity of samples was performed based on AP clustering,and then a probability filling formula was constructed to predict the values of missing labels. Secondly,the prior probability was used to define the proportion of label,which was combined with mutual information to develop the correlation metric for evaluating the correlation degree between features and label sets. Finally,a weak label feature selection algorithm was designed to effectively improve the classification performance of the weak label data. The simulation experimental results and analysis under six multi-label datasets show that the algorithm achieves better classification performance on multiple metrics and is superior to many related multi-label feature selection algorithms at present. All these can verify the effectiveness of the proposed algorithm.

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Last Update: 2022-09-15