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Enhanced SVM Classification Using Feature Variance Weighting in Linear and Non-linear Settings | ||
| Computational Sciences and Engineering | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 15 شهریور 1404 | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22124/cse.2025.31583.1118 | ||
| نویسندگان | ||
| Mostafa Eslami* 1؛ Afsaneh pourmoezi1؛ Ali Tavakoli2 | ||
| 1University of Mazandaran | ||
| 2University of mazandaran | ||
| چکیده | ||
| Support Vector Machine (SVM) is a powerful classification algorithm that separates samples by finding an optimal decision boundary. Its performance can degrade when feature variances differ across classes, potentially leading to suboptimal decision boundaries. A variance-weighted framework is proposed that reduces the influence of high-variance features while enhancing the impact of low-variance features, resulting in more accurate and robust decision boundaries. The method is applicable in both linear and nonlinear settings. Evaluation on synthetic datasets and real-world datasets, including Breast cancer and {\it a9a}, using cross-validation demonstrates that the variance-weighted SVM achieves higher accuracy and F1-score compared to soft SVM and LDM, particularly in scenarios with significant variance differences between classes. | ||
| کلیدواژه‌ها | ||
| Support vector machines؛ Classification؛ Variance-weighted features | ||
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