@misc{10481/67907,
year = {2021},
url = {http://hdl.handle.net/10481/67907},
abstract = {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.},
organization = {Taif University, Taif, Saudi Arabia 

TURSP-2020/125},
publisher = {IEEE Inst Electrical Electronics Engineers Inc},
keywords = {Teaching-Learning},
keywords = {Feature selection},
keywords = {Metaheuristics},
keywords = {Transfer function},
keywords = {Binarization},
title = {Teaching Learning-based Optimization with Evolutionary Binarization Schemes for Tackling Feature Selection Problems},
doi = {10.1109/ACCESS.2021.3064799},
author = {Thaer, Thaer and Castillo Valdivieso, Pedro Ángel},
}