Teaching Learning-based Optimization with Evolutionary Binarization Schemes for Tackling Feature Selection Problems
Metadatos
Mostrar el registro completo del ítemEditorial
IEEE Inst Electrical Electronics Engineers Inc
Materia
Teaching-Learning Feature selection Metaheuristics Transfer function Binarization
Fecha
2021Referencia bibliográfica
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]
Patrocinador
Taif University, Taif, Saudi Arabia TURSP-2020/125Resumen
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.