Learning the transfer function in binary metaheuristic algorithm for feature selection in classification problems

Abstract

One of the most challenging issues in pattern recognition is the data attribution selection process. Feature selection plays a key role in solving problems with high-dimensional data and is a fundamental step in pre-processing many classifications and machine learning problems. The feature selection method reduces the amount of data and increases the category precision. Unrelated data, which can lead to inappropriate classification, is thus removed to obtain fewer features. In this paper, the Binary Gray Wolf Optimization algorithm uses the Wrapper method for feature selection. The transfer function is an essential part of BGWO to map a continuous value to a binary value. In this study, eight transfer functions are divided into two families: S-shaped and V-shaped. Previous research has used only one transfer function for the whole algorithm, and all wolves in the whole algorithm deal with this transfer function. In this paper, each wolf has its own transfer function. Because algorithms are evolutionary meta-innovations and can optimize themselves, each wolf can play a role in the whole algorithm at any stage while optimizing itself and adapting to its community, and not just depend on one transfer function. In the proposed method, eight transfer functions are divided into two families, S-shaped and V-shaped. This article proposes two approaches for learning the transfer function, by selecting the transfer function and the slope of these functions. In the first approach, we add three or two binary bits to the initial population. If two bits are added, four modes of the transfer function are available, and if three bits are added, eight transfer functions are achievable. These bits are used as a criterion for selecting a predefined transfer function for each wolf. So, in the proposed method, each wolf has its transfer function. During the implementation of the algorithm, the wolves update their position according to the evaluation function and learning. In the second approach, ten or twenty-one binary bits are added to the initial population. If ten binary bits are used, we will have a transfer function, and $2^{10}$ coefficient modes are available for the slope of the transfer function. If twenty-one binary bits are used, we have two transfer functions available. So, there are $2^{10}$ modes for the gradient of the transfer function. These bits are used as a criterion for selecting the transfer function and the coefficient affecting the slope of these functions. In both ideas, after each iteration of the algorithm, the position of the wolves is updated and based on the evaluation function, the alpha wolf is identified and the transfer function is selected. With subsequent iterations, the algorithm learns and optimizes the transfer function to achieve the best feature selection with the smallest error. Experimental results on ten UCI datasets show that selecting the obtained feature subset with high classification accuracy is efficient.