The Comparison of Three Measures in Feature Selection
It has been known that either linear correlation or nonlinear correlation might exist between featureto- feature and feature-to-class in datasets. In this paper，we study the differences of selected feature subset when different kinds of measures are applied with same feature selection method in di...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2018-02-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1489 |
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| Summary: | It has been known that either linear correlation or nonlinear correlation might exist between featureto-
feature and feature-to-class in datasets. In this paper，we study the differences of selected feature subset when
different kinds of measures are applied with same feature selection method in different kinds of datasets. Three
representative linear or nonlinear measures，linear correlation coefficient，symmetrical uncertainty，and mutual
information are selected. By combining them with the fast correlation-based filter ( FCBF) feature selection
method，we make the comparison of selected feature subset from 8 gene microarray and image datasets.
Experimental results indicate that the feature subsets selected by linear correlation coefficient based FCBF obtain
better classification accuracy in gene microarray datasets than in image datasets，while mutual information and
symmetrical uncertainty based FCBF tend to obtain better results in image datasets. Moreover，symmetrical
uncertainty based FCBF is more robust in all datasets. |
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| ISSN: | 1007-2683 |