Maximum-relevance and maximum-diversity of positive ranks: A novel feature selection method

Sheikhi G., Altincay H.

EXPERT SYSTEMS WITH APPLICATIONS, cilt.158, 2020 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 158
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1016/j.eswa.2020.113499
  • Dergi Adı: EXPERT SYSTEMS WITH APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Public Affairs Index, Civil Engineering Abstracts
  • Orta Doğu Teknik Üniversitesi Kuzey Kıbrıs Kampüsü Adresli: Hayır

Özet

With the existing abundance of intelligent and expert systems, there is a need for selecting a subset of highly relevant features with low redundancy. In filter approaches, the feature subsets are iteratively computed by evaluating the candidate features in terms of their relevance with the target class and pair wise redundancies. The use mutual information-based metrics has been extensively studied as an approach to quantifying the relevance and redundancy of candidate features. In this study, a novel filter approach based on ranks of positive instances is proposed. In this approach, redundancy is replaced by diversity to quantify the complementarity of a candidate feature with respect to the already selected subset. Both relevance and diversity are computed in terms of the ranks of positive instances, which is analogous to the computation of the area under the receiver operating characteristic curve (AUC). Experiments conducted on 15 UCI and microarray gene expression data sets have confirmed that the proposed multivariate filter feature selection approach provides better performance scores when compared to other competing multivariate methods as well as benchmark univariate filters. (c) 2020 Elsevier Ltd. All rights reserved.