Infinite Multi-Label Feature Selection
عنوان مقاله: Infinite Multi-Label Feature Selection
شناسه ملی مقاله: CSCG04_010
منتشر شده در چهارمین کنفرانس بین المللی محاسبات نرم در سال 1400
شناسه ملی مقاله: CSCG04_010
منتشر شده در چهارمین کنفرانس بین المللی محاسبات نرم در سال 1400
مشخصات نویسندگان مقاله:
Sadegh Eskandari - Department Of Computer Science, University of Guilan. Rasht, Iran
خلاصه مقاله:
Sadegh Eskandari - Department Of Computer Science, University of Guilan. Rasht, Iran
Multi-label feature selection deals with the problem of dimensionality reduction of data in which an instance may belong to multiple class labels simultaneously. Because of computational concerns the existing multi-label feature selection algorithms are not able to consider all possible subsets of feature space in evaluating a candidate feature. This paper proposes a new approach that is able to consider all possible subsets of feature space in evaluating a feature. The proposed method uses the centrality concept in graph theory and reducts the feature evaluation function to finding path costs in feature adjacency graph. Experimental results demonstrate the superiority of the proposed method against state-of-the-art information-theoretical-based filter multi-label feature selection algorithms.
کلمات کلیدی: Feature Selection, Multi-Label Learning
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1418519/