Decision algorithms: a survey of rough set-theoretic methods
Fundamenta Informaticae - Special issue: intelligent information systems
A Rough Set-Based Hybrid Method to Text Categorization
WISE '01 Proceedings of the Second International Conference on Web Information Systems Engineering (WISE'01) Volume 1 - Volume 1
Reduct Generation and Classification of Gene Expression Data
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 01
Classification by instance-based learning algorithm
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Generating estimates of classification confidence for a case-based spam filter
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
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Most classification studies are done by using all the objects data. It is expected to classify objects by using some subsets data in the total data. A rough set based reduct is a minimal subset of features, which has almost the same discernible power as the entire conditional features. Here, we propose multiple reducts with confidence, which are followed by the k-nearest neighbor to classify documents to improve the classification accuracy. To select better multiple reducts for the classification, we develop a greedy algorithm for the multiple reducts, which is based on the selection of useful attributes for the documents classification. These proposed methods are verified to be effective in the classification on benchmark datasets from the Reuters 21578 data set.