A method for feature selection based on neighborhood relation and fuzzy decision
Wen Xin1，Li Deyu1，2*，Wang Suge1，2
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1.School of Computer & Information Technology，Shanxi University，Taiyuan，030006，China；
2．Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education，Shanxi University，Taiyuan，030006，China
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Published
2018-04-30
Abstract
The high dimensionality of existing data in the feature space makes learning procedure spend more time relatively and the classification performance could be affected. Neighborhood rough set model can be used to deal with the problem of feature selection，but the model cannot describe fuzziness of samples which exist in real world. Without description of fuzziness，the useful information could be lost. Thus，a novel feature selection model of single－label is built considering above analysis. The fuzzy membership values of every sample for all the equivalence classes are obtained by two different computational method of fuzzy membership and the smallest fuzzy membership value of every equivalence class is regarded as the threshold value of fuzzy membership. Then，neighborhood rough upper and lower approximation are redefined by the use of relation between the fuzzy membership values of neighbor samples and the threshold value of fuzzy membership in the current equivalence class researched. Further，the feature subspace is obtained by measuring the dependency of decision attribute on the feature subset. The experiment is carried out in seven public UCI datasets. The experimental results show that classification accuracy is further improved and the selected feature numbers are decreased obviously by comparing with the existing approaches which are used to select feature subspace.
Wen Xin1，Li Deyu1，2*，Wang Suge1，2.
A method for feature selection based on neighborhood relation and fuzzy decision[J]. Journal of Nanjing University(Natural Sciences), 2018, 54(4): 733
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