Abstract
Feature selection is a satisfactory optimization problem. Most feature selection methods did not consider the cost of feature extraction and the automatic decision of feature subset dimension. So a novel approach called satisfactory feature selection method (SFSM) was proposed. SFSM integrated feature extraction with feature selection and considered classification performance of feature samples, the dimension of feature subset and the complexity of feature extraction simultaneously. Experimental results show that SFSM selects more satisfying feature subset than sequential forward selection using distance criterion (SFSDC) and the method presented by Tiejun Lü (GADC). Also, SFSM achieves higher accurate recognition rate than original feature set, SFSDC and GADC, which verifies the validity of the proposed method.
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Zhang, G., Jin, W., Hu, L. (2004). A Novel Feature Selection Approach and Its Application. In: Zhang, J., He, JH., Fu, Y. (eds) Computational and Information Science. CIS 2004. Lecture Notes in Computer Science, vol 3314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30497-5_104
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DOI: https://doi.org/10.1007/978-3-540-30497-5_104
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