Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Scalable Feature Selection Using Rough Set Theory
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
AI Communications - Special issue on Artificial intelligence advances in China
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Rough set based approaches to feature selection for Case-Based Reasoning classifiers
Pattern Recognition Letters
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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.