An evolutionary parallel multiobjective feature selection framework

Kiziloz H. E., Deniz A.

Computers and Industrial Engineering, cilt.159, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 159
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.cie.2021.107481
  • Dergi Adı: Computers and Industrial Engineering
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, DIALNET, Civil Engineering Abstracts
  • Anahtar Kelimeler: Feature selection, Multiobjective optimization, Parallel processing, Evolutionary computation, GENETIC ALGORITHM, OPTIMIZATION, CLASSIFICATION
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

© 2021 Elsevier LtdFeature selection has become an indispensable preprocessing step in data mining problems as high amount of data become prevalent with the advances in technology. The objective of feature selection is twofold: reducing data amount and improving learning performance. In this study, we leverage the multi-core nature of a regular PC to build a robust framework for feature selection. This framework executes the feature selection algorithm on four processors, in parallel. As per the No Free Lunch Theorem, we facilitate 40 different execution settings for the processors by employing two multiobjective selection algorithms, four initial population generation methods, and five machine learning techniques. Besides, we introduce six setting selection schemes to decide the most fruitful setting for each processor. We carry out extensive experiments on 11 UCI benchmark datasets and analyze the results with statistical tests. Finally, we compare our proposed method with state-of-the-art studies and record remarkable improvement in terms of maximum accuracy.