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Hybrid Filter-Wrapper Feature Selection using Equilibrium Optimization | ||
| Journal of Algorithms and Computation | ||
| دوره 55، شماره 1، شهریور 2023، صفحه 101-122 اصل مقاله (3.11 M) | ||
| شناسه دیجیتال (DOI): 10.22059/jac.2023.92772 | ||
| نویسندگان | ||
| Mohammad Ansari Shiri1؛ Najme Mansouri* 2 | ||
| 1Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran | ||
| 2Department of Computer science, Shahid Bahonar University of Kerman, Kerman, Iran | ||
| چکیده | ||
| The topic of feature selection has become one of the hottest subjects in machine learning over the last few years. The results of evolutionary algorithm selection have also been promising, along with standard feature selection algorithms. For K-Nearest Neighbor (KNN) classification, this paper presents a hybrid filter-wrapper algorithm based on Equilibrium Optimization (EO). With respect to the selected feature subset, the filter model is based on a composite measure of feature relevance and redundancy. The wrapper model consists of a binary Equilibrium Optimization (BEO). The hybrid algorithm is called filter-based BEO (FBBEO). By combining filters and wrappers, FBBEO achieves a unique combination of efficiency and accuracy. In the experiment, 11 standard datasets from the UCI repository were utilized. Results indicate that the proposed method is effective in improving the classification accuracy and selecting the best optimal features subsets with the least number of features. | ||
| کلیدواژه‌ها | ||
| Feature selection؛ Classification؛ Wrapper؛ Filter؛ Equilibrium Optimization | ||
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آمار تعداد مشاهده مقاله: 223 تعداد دریافت فایل اصل مقاله: 241 |
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