An intelligent feature selection method using binary teaching-learning based optimization algorithm and ANN

doi:10.1016/j.chemolab.2023.104880

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An intelligent feature selection method using binary teaching-learning based optimization algorithm and ANN

2023-09-01

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Mahdieh Khorashadizade

Soodeh Hosseini *

DOI：10.1016/j.chemolab.2023.104880

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Abstract

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The most challenging issue in dealing with big datasets is the large number of their dimensions. Feature selection is a technique for reducing the dimensionality of datasets by removing irrelevant and useless features from the dataset, finally selecting the most crucial features in the dataset, will ultimately increase the algorithm's efficiency. This paper presents a novel feature selection procedure that includes a binary-modified teaching learning-based optimization algorithm (BMTLBO). One of the most effective and popular methods is the TLBO algorithm. Although this algorithm has a good convergence rate, there is a chance to get stuck in a local optimum. Our goal is to balance between exploration and exploitation capabilities. The proposed method consists of two parts: First, the BMTLBO algorithm, which includes the improved binary version of the basic algorithm, is used for the feature selection problem. The idea of a pool is used in this algorithm that boosts the population's diversity while enhancing the algorithm's precision and convergence rate. Second, we are used from improved TLBO algorithm with the self-learning phase (SLTLBO) for neural network training to show the application of the classification problem to evaluate the performance of the procedures of the method. We are tested the proposed method on 14 datasets in the term of classification accuracy and the number of features. Additionally, a different chemical data set named Biodegradable is used to assess the proposed method. The evaluation results demonstrate that, in terms of accuracy, convergence rate, and success in achieving promising solutions, the proposed algorithm outperforms all other optimization algorithms compared. The results are very promising and close to optimal.

Keywords:

Feature selection

Classification problem

Evolutionary algorithm

Artificial neural network