Dual-Channel Fuzzy Interaction Information Fused Feature Selection With Fuzzy Sparse and Shared Granularities
Küçük Resim Yok
Tarih
14.11.2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Fuzzy information granularity is an effective granular computation approach for feature evaluation and selection. However, most existing methods rely on a single granulation channel, neglecting different granularity representations. In this article, a novel dual-channel fuzzy interaction information fused feature selection with fuzzy sparse and shared granularities is proposed. It mainly comprises the following three parts. First, a dual-channel framework is introduced to construct the fuzzy information granularity from two different strategies. One channel employs sparse mutual strategy to form the sparse representation-based fuzzy information granularity, while the other constructs the fuzzy shared information granularity with a novel fuzzy semi-ball. Second, in each channel, the criteria of maximum relevancy, minimum redundancy, and maximum interaction is adopted to access feature correlation and perform feature ranking. Third, the two feature sequences derived from the dual-channel are fused to form a final feature sequence based on the within-class and between-class mechanism. To validate the efficacy of the proposed method, experimental validations on 15 datasets and schizophrenia data are conducted. The results show that the proposed method outperforms other algorithms in classification accuracy and statistical analysis. Moreover, its superiority regarding accuracy can be demonstrated in the experiments of schizophrenia detection, where it performs well in recognizing schizophrenia through visual interpretation.
Açıklama
Anahtar Kelimeler
feature measurefeature selectionDual-channelfuzzy information granularityfuzzy information granularityfeature selectionfuzzy information granularity
Kaynak
IEEE TRANSACTIONS ON FUZZY SYSTEMS
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
32
Sayı
11
Künye
Ju, H., Fan, X., Ding, W., Huang, J., Xu, S., Yang, X., & Pedrycz, W. (2024). Dual-channel fuzzy interaction information fused feature selection with fuzzy sparse and shared granularities. IEEE Transactions on Fuzzy Systems.
