Teaching Learning-based Optimization with Evolutionary Binarization Schemes for Tackling Feature Selection Problems
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IEEE Inst Electrical Electronics Engineers Inc
Teaching-LearningFeature selectionMetaheuristicsTransfer functionBinarization
Thaher, T., Mafarja, M., Turabieh, H., Castillo, P. A., Faris, H., & Aljarah, I. (2021). Teaching Learning-Based Optimization With Evolutionary Binarization Schemes for Tackling Feature Selection Problems. IEEE Access, 9, 41082-41103. [doi:10.1109/ACCESS.2021.3064799]
SponsorshipTaif University, Taif, Saudi Arabia TURSP-2020/125
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.