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Feature Selection With Fuzzy-Rough Minimum Classification Error Criterion
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DOI：10.1109/TFUZZ.2021.3097811.png)
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Abstract
En 中文
Classical fuzzy rough set often uses fuzzy rough dependency as an evaluation function of feature selection. However, this function only retains the maximum membership degree of a sample to one decision class, it cannot describe the classification error. Therefore, in this article, a novel criterion function for feature selection is proposed to overcome this weakness. To characterize the classification error rate, we first introduce a class of irreflexive and symmetric fuzzy binary relations to redefine the concepts of fuzzy rough approximations. Then, we propose a novel concept of dependency: inner product dependency to describe the classification error and construct a criterion function to evaluate the importance of candidate features. The proposed criterion function not only can maintain a maximum dependency function, but also guarantees the minimum classification error. The experimental analysis shows that the proposed criterion function is effective for datasets with a large overlap between different categories.
Keywords:
Rough sets
Feature extraction
Error analysis
Classification algorithms
Data models
Task analysis
Fuzzy sets
Dependency function
feature selection
fuzzy inner product
fuzzy rough set
Journal
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11.9
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5.0K
Citations:
2.9W
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