1. 基于熵权法的过滤式特征选择算法.
- Author
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李占山, 杨云凯, and 张家晨
- Subjects
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INFORMATION theory , *ENTROPY , *FEATURE selection , *ALGORITHMS , *CLASSIFICATION , *BRAIN-computer interfaces , *REDUNDANCY in engineering , *TASKS - Abstract
Mutual information-based filtering feature selection algorithms are often limited to the metric of mutual information. In order to circumvent the limitations of adopting only mutual information, a distance metric-based algorithm RReliefF is introduced on the basis of mutual information to obtain better filtering criteria. RReliefF is used for the classification tasks to measure the relevance between features and labels. In addition, maximal information coefficient(MIC) is used to measure the redundancy between features and the relevance between features and labels. Finally, entropy weight method is applied to objectively weigh the MIC and RReliefF. On this basis, a filtering feature selection algorithm based on entropy weight method(FFSBEWM) is proposed. Comparing experiments carried out on 13 data sets show that the average classification accuracy and highest classification accuracy of the feature subsets selected by the proposed algorithm are higher than those of the comparison algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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