Teaching Learning-based Optimization with Evolutionary Binarization Schemes for Tackling Feature Selection Problems Thaer, Thaer Castillo Valdivieso, Pedro Ángel Teaching-Learning Feature selection Metaheuristics Transfer function Binarization 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. 2021-04-12T08:30:01Z 2021-04-12T08:30:01Z 2021 info:eu-repo/semantics/article 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] http://hdl.handle.net/10481/67907 10.1109/ACCESS.2021.3064799 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España IEEE Inst Electrical Electronics Engineers Inc