GBRUN: A Gradient Search-based Binary Runge Kutta Optimizer for Feature Selection

Dou Z., Chu S., Zhuang Z., YILDIZ A. R., Pan J.

JOURNAL OF INTERNET TECHNOLOGY, cilt.25, sa.3, ss.341-353, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 25 Sayı: 3
  • Basım Tarihi: 2024
  • Doi Numarası: 10.53106/160792642024052503001
  • Dergi Adı: JOURNAL OF INTERNET TECHNOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Compendex, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.341-353
  • Bursa Uludağ Üniversitesi Adresli: Hayır

Özet

Feature selection (FS) is a pre-processing technique for data dimensionality reduction in machine learning and data mining algorithms. FS technique reduces the number of features and improves the model generalization ability. This study presents a Gradient Search-based Binary Runge Kutta Optimizer (GBRUN) for solving the FS problem of high-dimensional. First, the proposed method converts the continuous Runge Kutta optimizer (RUN) into a binary version through S-, V-, and U-shaped transfer functions. Second, a gradient search method is introduced to improve the exploration capability of the algorithm. Five standard performance of the GBRUN algorithm. The experimental results show that GBRUN has better performance than in this manuscript, using the GBRUN algorithm to select algorithms have better performance than other algorithms.