A Probabilistic Multi-Objective Artificial Bee Colony Algorithm for Gene Selection

Özger Z. B., Bolat B., Diri B.

JOURNAL OF UNIVERSAL COMPUTER SCIENCE, cilt.25, ss.418-443, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 25
  • Basım Tarihi: 2019
  • Dergi Adı: JOURNAL OF UNIVERSAL COMPUTER SCIENCE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.418-443
  • Anahtar Kelimeler: microarray, normalization, gene selection, machine learning, artificial bee colony, MICROARRAY DATA, CLASSIFICATION, NORMALIZATION
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

Microarray technology is widely used to report gene expression data. The inclusion of many features and few samples is one of the characteristic features of this platform. In order to define significant genes for a particular disease, the problem of high-dimensionality microarray data should be overcome. The Artificial Bee Colony (ABC) Algorithm is a successful meta-heuristic algorithm that solves optimization problems effectively. In this paper, we propose a hybrid gene selection method for discriminatively selecting genes. We propose a new probabilistic binary Artificial Bee Colony Algorithm, namely PrBABC, that is hybridized with three different filter methods. The proposed method is applied to nine microarray datasets in order to detect distinctive genes for classifying cancer data. Results are compared with other well-known meta-heuristic algorithms: Binary Differential Evolution Algorithm (BinDE), Binary Particle Swarm Optimization Algorithm (BinPSO), and Genetic Algorithm (GA), as well as with other methods in the literature. Experimental results show that the probabilistic self-adaptive learning strategy integrated into the employed-bee phase can boost classification accuracy with a minimal number of genes.