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Communication Dans Un Congrès Année : 2022

ROSES: A Novel Semi-supervised Feature Selector

Résumé

In this study, a novel ROugh set based Semi-supervised fEature Selector (Roses) was developed to pre-process partially labeled data. The main innovations of our Roses are: 1) the selected features over labeled samples laid the foundation for further searching qualified features over unlabeled samples; 2) a new granularity related measure was designed to quickly evaluate features. Through testing four different ratios (20%, 40%, 60%, 80%) of labeled samples, the experimental results over 15 UCI datasets demonstrated that our framework is superior to the other five popular partially labeled data feature selectors: 1) the feature subsets identified by Roses offer competitive classification performances; 2) Roses is good at seeking a balance between efficiency of searching features and effectiveness of the selected features.
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Dates et versions

hal-04178734 , version 1 (08-08-2023)

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Xiaoyu Zhang, Keyu Liu, Jing Ba, Xin Yang, Xibei Yang. ROSES: A Novel Semi-supervised Feature Selector. 12th International Conference on Intelligent Information Processing (IIP), May 2022, Qingdao, China. pp.336-347, ⟨10.1007/978-3-031-03948-5_27⟩. ⟨hal-04178734⟩
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