Teaching Learning-Based Optimization With Evolutionary Binarization Schemes for Tackling Feature Selection Problems

IEEE Access

Pedro A. Castillo¹  Majdi Mafarja²  Thaer Thaher³  Hamza Turabieh⁴  Ibrahim Aljarah⁵  Hossam Faris⁶ 

[1] Department of Computer Architecture and Technology, University of Granada, Granada, Spain;Department of Computer Science, Birzeit University, Ramallah, Palestine;Department of Engineering and Technology Sciences, Arab American University, Ramallah, Palestine;Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia;King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan;School of Computing and Informatics, Al Hussein Technical University, Amman, Jordan;

关键词: Teaching-learning;    feature selection;    metaheuristic;    transfer function;    binarization;   

DOI  :  10.1109/ACCESS.2021.3064799

来源: DOAJ

【 摘 要 】

Machine learning techniques heavily rely on available training data in a data set. Certain features in the data can interfere with the learning process, so it is required to remove irrelevant and redundant features to build a robust training model. As such, several feature selection techniques are usually applied in a pre-processing phase to obtain the most appropriate set of features and improve the overall learning process. In this paper, a new feature selection approach is proposed based on a modified Teaching-Learning-based Optimization (TLBO) combined with four new binarization methods: the Elitist, the Elitist Roulette, the Elitist Tournament, and the Rank-based method. The influence of these binarization methods is studied and compared to other state-of-the-art techniques. The experimental results such as Shapiro-Wilk normality and Wilcoxon ranksum test show that both transfer functions and binarization approaches have a significant influence on the effectiveness of the binary TLBO. The experiments show that choosing a fitting transfer function along with a suitable binarization method has a substantial impact on the exploratory and exploitative potentials of the feature selection technique.

【 授权许可】

Unknown