Abstract
There are generally many redundant and irrelevant features in high-dimensional datasets, which leads to the decline of classification performance and the extension of execution time. To tackle this problem, feature selection techniques are used to screen out redundant and irrelevant features. The artificial bee colony (ABC) algorithm is a popular meta-heuristic algorithm with high exploration and low exploitation capacities. To balance between both capacities of the ABC algorithm, a novel ABC framework is proposed in this paper. Specifically, the solutions are first updated by the process of employing bees to retain the original exploration ability, so that the algorithm can explore the solution space extensively. Then, the solutions are modified by the updating mechanism of an algorithm with strong exploitation ability in the onlooker bee phase. Finally, we remove the scout bee phase from the framework, which can not only reduce the exploration ability but also speed up the algorithm. In order to verify our idea, the operators of the grey wolf optimization (GWO) algorithm and whale optimization algorithm (WOA) are introduced into the framework to enhance the exploitation capability of onlooker bees, named BABCGWO and BABCWOA, respectively. It has been found that these two algorithms are superior to four state-of-the-art feature selection algorithms using 12 high-dimensional datasets, in terms of the classification error rate, size of feature subset and execution speed.
Highlights
In order to trade off the exploitation and exploration abilities of artificial bee colony (ABC), we use operators with strong exploitation abilities to enhance the exploitation ability in the phase of onlooker bee; This paper analyzes the functional behavior of the scout bee phase and finds that this phase may be redundant while dealing with high-dimensional feature selection (FS) problems, and so eliminating this phase can reduce the computational time of the algorithm; The proposed framework is designed as a general framework that can be used to adapt many ABC variants for the FS problems
This paper introduces the operators of the grey wolf optimization (GWO) algorithm and whale optimization algorithm (WOA) algorithm into our proposed framework to verify its validity
We can see that the diversity of ABC is obviously higher than that of other algorithms on all datasets, except DBWorld and Pixraw10P
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. We propose a new framework to enhance the exploitation performance of the ABC algorithm, so as to realize a trade-off between the exploration and exploitation capabilities of the FS method, and raise the optimization efficiency and effectiveness. In order to trade off the exploitation and exploration abilities of ABC, we use operators with strong exploitation abilities to enhance the exploitation ability in the phase of onlooker bee; This paper analyzes the functional behavior of the scout bee phase and finds that this phase may be redundant while dealing with high-dimensional FS problems, and so eliminating this phase can reduce the computational time of the algorithm; The proposed framework is designed as a general framework that can be used to adapt many ABC variants for the FS problems.
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