Multi Hive Artificial Bee Colony Programming for high dimensional symbolic regression with feature selection

Arslan S., Öztürk C.

APPLIED SOFT COMPUTING, cilt.78, ss.515-527, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 78
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1016/j.asoc.2019.03.014
  • Dergi Adı: APPLIED SOFT COMPUTING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.515-527
  • Anahtar Kelimeler: Feature selection, Genetic programming, Artificial bee colony programming, Multi hive artificial bee colony programming, High dimension data, IMPROVING GENERALIZATION, GENERALIZATION ABILITY, ALGORITHM, PERFORMANCE
  • Erciyes Üniversitesi Adresli: Evet

Özet

Feature selection is a process that provides model extraction by specifying necessary or related features and improves generalization. The Artificial Bee Colony (ABC) algorithm is one of the most popular optimization algorithms inspired on swarm intelligence developed by simulating the search behavior of honey bees. Artificial Bee Colony Programming (ABCP) is a recently proposed high level automatic programming technique for a Symbolic Regression (SR) problem based on the ABC algorithm. In this paper, a new feature selection method based on ABCP is proposed, Multi Hive ABCP (MHABCP) for high-dimensional SR problems. The learning ability and generalization performance of the proposed MHABCP is investigated using synthetic and real high-dimensional SR datasets and is compared with basic ABCP and GP automatic programming methods. Experimental results show that MHABCP has better performance choosing relevant features in high dimensional SR problems and generalization than other methods. (C) 2019 Elsevier B.V. All rights reserved.