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Fuzzy K-Nearest Neighbor in Classification and Regression | ||
| Journal of Data Science and Modeling | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 08 خرداد 1404 اصل مقاله (1.15 M) | ||
| نوع مقاله: Research Manuscript | ||
| شناسه دیجیتال (DOI): 10.22054/jdsm.2025.83407.1060 | ||
| نویسندگان | ||
| Zahra Behdani؛ Majid Darehmiraki* | ||
| Department of mathematics, Behbahan Khatam Alanbi University of Technology | ||
| چکیده | ||
| The Fuzzy K-Nearest Neighbour (FKNN) method is a classification approach that integrates fuzzy theories with the K-Nearest Neighbour classifier. The algorithm computes the degree of membership for a given dataset within each class and then chooses the class with the highest degree of membership as the assigned classification outcome. This algorithm has several applications in regression problems. When the mathematical model of the data is not known, this method can be used to estimate and approximate the value of the response variable. This paper introduces a method, which incorporates a parameter distance measure to empower decision makers to make precise selections across several levels. Furthermore, we provide an analysis of the algorithm's strengths and shortcomings, as well as a comprehensive explanation of the distinctions between the closest neighbour approach in tasks of classification and regression. Finally, to further elucidate the principles, we present a range of examples that demonstrate the application of closest neighbour algorithms in the classification and regression of fuzzy numbers. | ||
| کلیدواژه‌ها | ||
| Fuzzy؛ K-Nearest Neighbor؛ Distance؛ Classification؛ Regression | ||
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آمار تعداد مشاهده مقاله: 3 تعداد دریافت فایل اصل مقاله: 5 |
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