Abstract
Feature selection carries significance in the outcome of any classification or regression task. Exercising evolutionary computation algorithms in feature selection has led to the construction of efficient discrete optimization algorithms. In this paper, a modified backtracking search algorithm is employed to perform wrapper-based feature selection, where two modifications of the standard backtracking search algorithm are adopted. The first one concentrates on utilizing a particle ranking operator regarding the current population. The second one focuses on removing the case of using a single particle on the mutation process. Then, the implementation of the above algorithm in feature selection is carried out in terms of two general frameworks, which originally were developed for the particle swarm optimization. The first framework is based on the binary and the second on the set-based particle swarm optimization. The experimental analysis shows that the above variants of the backtracking search algorithm perform equally well on the classification of several datasets.
Highlights
Feature selection (FS) is defined as the problem of choosing an optimal subset of features for use in a classification or regression model
Setbased Particle Swarm Optimization (PSO) algorithms define particle positions and velocities as sets and construct interactions that lead to new velocityposition pairs
We investigate the feasibility of employing Backtracking Search Optimization (BSA) for combinatorial optimization within the context of feature selection
Summary
Feature selection (FS) is defined as the problem of choosing an optimal subset of features for use in a classification or regression model. It consists of two main components, namely a search technique for proposing new feature subsets and an evaluation function for scoring them. Evolutionary algorithms have been used extensively to search through the space of possible features These algorithms are based on three key concepts, namely particles, representing the candidate solutions to the problem, positions, which are the values of the particles at each iteration and velocities, that are directions along which particles’ positions are required to change. Conclusions and future work are presented in the last section
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