Feature Selection With Controlled Redundancy in a Fuzzy Rule Based Framework
Autor: | I-Fang Chung, Yi-Cheng Chen, Nikhil R. Pal |
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Rok vydání: | 2018 |
Předmět: |
Fuzzy rule
business.industry Applied Mathematics 0206 medical engineering System identification Pattern recognition Feature selection 02 engineering and technology Mutual information computer.software_genre Support vector machine Nonlinear system Computational Theory and Mathematics Artificial Intelligence Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Minimum redundancy feature selection Entropy (information theory) 020201 artificial intelligence & image processing Data mining Artificial intelligence business computer 020602 bioinformatics Mathematics |
Zdroj: | IEEE Transactions on Fuzzy Systems. 26:734-748 |
ISSN: | 1941-0034 1063-6706 |
Popis: | Features that have good predictive power for classes or output variables are useful features and hence most feature selection methods try to find them. However, since there may be high correlation or nonlinear dependence between such good features, we may obtain a comparable performance even when we use only a few of those good features. Thus, a feature selection method should select useful features with controlled redundancy. In this paper, we propose a novel learning method that imposes a penalty on the use of dependent/correlated features during system identification along with feature selection. This feature selection scheme can choose good features, discard indifferent, and derogatory features, and can control the level of redundancy in the set of selected features. This is probably the first attempt to feature selection with redundancy control using a fuzzy rule based framework. We have demonstrated the effectiveness of this method by utilizing a tenfold cross-validation setup on a synthetic dataset as well as on several commonly used datasets for classification problems. We have also compared our results with some state-of-the-art methods. |
Databáze: | OpenAIRE |
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