A Review on Hybrid Artificial Bee Colony for Feature Selection
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Date
2021-08-30
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Global Journal of Advanced Research
Abstract
Due to the presence of redundant and irrelevant features in the dataset, the feature space's high dimensionality has an impact on classification accuracies and computational complexity. Feature Selection gets the most relevant and valuable information and aids in classification speed. Since finding the suitable, optimal feature subset is critical, feature selection is viewed as an optimization problem. One of the efficient nature-inspired optimization techniques for handling combinatorial optimization issues is the Artificial Bee Colony algorithm. It has no sensitive control parameters and has been demonstrated to compete with other well-known algorithms. However, it has a poor local search performance, with the equation of solution search in ABC performing well for exploration but poorly for exploitation. Furthermore, it has a quick convergence rate and can thus become caught in local optima for some complex multimodal situations. Since its introduction, much research has been conducted to address these issues in order to make ABC more efficient and applicable to a wide range of applications. This paper provides an overview of ABC advances, applications, comparative performance, and future research opportunities.
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Keywords
Feature selection, swarm intelligent algorithms, artificial bee colony, exploration, exploitation, classification