A Robust Adaptive Hierarchical Learning Crow Search Algorithm for Feature Selection

Abstract

Feature selection is a multi-objective problem, which can eliminate irrelevant and redundant features and improve the accuracy of classification at the same time. Feature selection is a great challenge to balance the conflict between the two goals of selection accuracy and feature selection ratio. The evolutionary algorithm has been proved to be suitable for feature selection. Recently, a new meta-heuristic algorithm named the crow search algorithm has been applied to the problem of feature selection. This algorithm has the advantages of few parameters and achieved good results. However, due to the lack of diversity in late iterations, the algorithm falls into local optimal problems. To solve this problem, we propose the adaptive hierarchical learning crow search algorithm (AHL-CSA). Firstly, an adaptive hierarchical learning technique was used to adaptive divide the crow population into several layers, with each layer learning from the top layer particles and the topmost layer particles learning from each other. This strategy encourages more exploration by lower individuals and more exploitation by higher individuals, thus improving the diversity of the population. In addition, in order to make full use of the search information of each level in the population and reduce the impact of local optimization on the overall search performance of the algorithm, we introduce an information sharing mechanism to help adjust the search direction of the population and improve the convergence accuracy of the algorithm. Finally, different difference operators are used to update the positions of particles at different levels. The diversity of the population is further improved by using different difference operators. The performance of the method was tested on 18 standard UCI datasets and compared with eight other representative algorithms. The comparison of experimental results shows that the proposed algorithm is superior to other competitive algorithms. Furthermore, the Wilcoxon rank-sum test was used to verify the validity of the results.

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“…In this work, we sought to use diverse algorithms inspired by evolution, physical mechanisms and collective intelligence, as mentioned in the work of Agrawa et al 28 It should be noted that the algorithms applied were quite simple, trying to preserve the minimum version of each one to encourage equality of conditions, so they cannot be directly comparable with those of other works that present cumulative improvements said algorithms. 20,29,21,22,23 Relevant differences with said works will be discussed below.…”

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“…The reason is that certain features have a significant impact, while others may be completely useless for emotion recognition. Feature selection methods simplify the task of selecting the most relevant features for classification algorithms [7,8]. These methods mainly eliminate the loss and overfitting problems caused by the curse of dimensionality, and improve the model's generalization.…”

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