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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Feature Selection by Ordered Rough Set Based Feature Weighting
verfasst von : Qasem A. Al-Radaideh, Md Nasir Sulaiman, Mohd Hasan Selamat, Hamidah Ibrahim
Erschienen in: Database and Expert Systems Applications
Verlag: Springer Berlin Heidelberg
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The aim of feature subset selection is to reduce the complexity of an induction system by eliminating irrelevant and redundant features. Selecting the right set of features for classification task is one of the most important problems in designing a good classifier. In this paper we propose a feature selection approach based on rough set based feature weighting. In the approach the features are weighted and ranked in descending order. An incremental forward interleaved selection process is used to determine the best feature set with highest possible classification accuracy. The approach is experimented and tested using some standard datasets. The experiments carried out are to evaluate the influence of the feature pre-selection on the prediction accuracy of the rough classifier. The results showed that the accuracy could be improved with an appropriate feature pre-selection phase.