Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework
| Autor: | Jing Wang, Xiuli Wang, Yuanzi Zhang, Shiguo Huang, Xiaolin Li |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Speedup
Computer science Industrial engineering. Management engineering Word error rate Feature selection High dimensional T55.4-60.8 Machine learning computer.software_genre Execution time Theoretical Computer Science exploration–exploitation balance feature selection artificial bee colony algorithm Numerical Analysis business.industry Process (computing) QA75.5-76.95 Artificial bee colony algorithm Computational Mathematics Computational Theory and Mathematics Feature (computer vision) Electronic computers. Computer science Artificial intelligence business computer high dimensionality |
| Zdroj: | Algorithms, Vol 14, Iss 324, p 324 (2021) Algorithms Volume 14 Issue 11 |
| ISSN: | 1999-4893 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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