Multi-association evidential feature selection and its application to identifying schizophrenia

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorJu, Hengrong
dc.contributor.authorFan, Xiaoxue
dc.contributor.authorDing, Weiping
dc.contributor.authorHuang, Jiashuang
dc.contributor.authorPedrycz, Witold
dc.contributor.authorYang, Xibei
dc.date.accessioned2025-04-18T10:10:06Z
dc.date.available2025-04-18T10:10:06Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractGranular Computing (GrC)-based feature selection can remove redundant features from a massive amount of data and improve the efficiency of information processing. However, the existing method of neighborhood-based information granule only considers the distance between samples, ignoring other significant relationships existing between them. To fill this gap, this paper proposes a novel feature selection approach based on two-step multi-association neighborhood evidence entropy. This approach is constructed in three phases. Firstly, adaptive k value corresponding to each sample in sparse representation method is determined. Sparse correlation and distance measure are fused to form a multi-association information granule. Then, the samples in the multi-association information granule are estimated and weak-related information is removed to constitute a two-step multi-association information granule. Secondly, sparse correlation information is processed using Dempster-Shafer evidence theory, and a new credibility-based function is developed. In addition, the credibility is used to construct a novel neighborhood evidence entropy, which can effectively reflect the uncertainty of data. Thirdly, the proposed neighborhood evidence entropy is applied to assess the importance of features. As a result, several vital features are selected. The experimental results on twelve datasets demonstrate that the effectiveness of the proposed method is superior to other algorithms in construction of information granules and classification accuracy, respectively. Finally, the proposed method is applied to the selection of brain regions in schizophrenia. It can effectively analyze the lesions of schizophrenia and improve the prediction of the disorder. The code is available at https://github.com/fxx-Aurora/TMAE-FS/tree/main. © 2024 Elsevier Inc.
dc.description.sponsorshipThe authors would like to express the sincere appreciation to the editor and anonymous reviewers for their insightful comments, which greatly improve the quality of this paper. This work is supported by the National Natural Science Foundation of China (No. 62006128, No. 62102199, No. 62076111 and No. 61976120), Natural Science Key Foundation of Jiangsu Education Department (No. 21KJA510004), Jiangsu Doctor Program of Entrepreneurship and Innovation (No. (2020) 30986).
dc.identifier.citationJu, H., Fan, X., Ding, W., Huang, J., Pedrycz, W., & Yang, X. (2024). Multi-association evidential feature selection and its application to identifying schizophrenia. Information Sciences, 674, 120647.
dc.identifier.doi10.1016/j.ins.2024.120647
dc.identifier.issn00200255
dc.identifier.scopus2-s2.0-85193427830
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1016/j.ins.2024.120647
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6973
dc.identifier.volume674
dc.identifier.wosWOS:001391627200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherElsevier Inc.
dc.relation.ispartofInformation Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectFeature Selection
dc.subjectGranular Computing
dc.subjectInformation Fusion
dc.subjectMulti-Association
dc.subjectNeighborhood Evidence Entropy
dc.titleMulti-association evidential feature selection and its application to identifying schizophrenia
dc.typeArticle

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