201. 非一致性引导的无监督特征选择.
- Author
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王莹莹 and 衍鹏
- Subjects
- *
ROUGH sets , *ALGORITHMS , *FEATURE selection , *FUZZY sets - Abstract
Because feature selection in an unsupervised environment lacks dependence on category information, this paper proposed a disagreement measure (DAM) with the use of the fuzzy rough set theory. DAM measured the degree of difference in meaning of fuzzy equivalence of any two feature sets or features. On this basis, this paper proposed the DAMUFS unsupervised feature selection algorithm. The DAMUFS algorithm could select feature subsets that contained more information under unsupervised conditions, while also ensuring that the attribute redundancy in the feature subset was as small as possible. The experiment compared the classification performance of DAMUFS algorithm with some unsupervised and supervised feature selection algorithms on multiple data sets. And the results prove the effectiveness of DAMUFS algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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