Cluster based Feature Subset Selection |
Author(s): |
Gurusamy P , M A COLLEGE OF ENGINEERING KOTHAMNAGALAM; Neethu Subash, M A COLLEGE OF ENGINEERING KOTHAMNAGALAM |
Keywords: |
Clustering, Subset Selection, CBFS algorithm |
Abstract |
In data mining, the Feature selection is the process of selecting a subset of relevant features for use in model construction. The central assumption when using a feature selection technique is that the data contain many redundant or irrelevant features. Of the many feature subset selection algorithms, some can effectively eliminate irrelevant features but fail to handle redundant features yet some of others can eliminate the irrelevant while taking care of the redundant features. Our proposed CBFS algorithm eliminate them very efficiently. Traditionally, feature subset selection research has focused on searching for relevant features. The results on the five text and micro array data sets show that CBFS can effectively identify irrelevant and redundant features. And CBFS can not only efficiently reduce the feature space, but also can significantly improve the performance of the four well-known classifiers. |
Other Details |
Paper ID: SPDM041 Published in: Volume : 1, Issue : 2 Publication Date: 01/12/2015 Page(s): 32-35 |
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