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Reducing dimensionality using copula-based extropy | ||
| Iranian Journal of Fuzzy Systems | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 20 آبان 1405 اصل مقاله (2.66 M) | ||
| نوع مقاله: Original Manuscript | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2026.55828.9912 | ||
| نویسندگان | ||
| F. Arad1؛ A. sheikhi* 1؛ F. Keynia2 | ||
| 1Dept of Stat. Shahid Bahonar University (SUBK), Kerman, Iran | ||
| 2Department of Energy Management and Optimization, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran | ||
| چکیده | ||
| When dealing with high-dimensional datasets, dimensionality reduction techniques become particularly important. Feature selection is one such method, aiming to select a subset of features such that the selected features have the highest relevance to the target variable and the lowest redundancy on each other. In this paper, we intend to utilize extropy, as a complement to entropy, for feature selection. Since common feature selection methods often ignore the dependencies between variables, we will consider these dependencies in the selection process by using copula functions. To this end, a feature selection algorithm based on copula functions and extropy is introduced, and the results of its implementation on real and simulated datasets are presented. More specific, we assess our propose approach with 3 scenarios in simulation study and we used it in feature selection of 3 real word dataset. These results demonstrate that while our proposed feature selection method significantly outperforms other alternatives, it benefits from capturing the nonlinear dependencies. | ||
| کلیدواژه‌ها | ||
| Mutual information؛ extropy؛ copula؛ dimension reduction؛ feature selection | ||
| مراجع | ||
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