A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
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
Modified reducts and their processing for nearest neighbor classification
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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Reduct in rough set is a minimal subset of features, which has almost the same discernible power as the entire features. Then, there are relations between reducts and the classification classes. Here, we propose multiple reducts which are followed by the k-nearest neighbor with confidence to classify documents with higher classification accuracy. To improve the classification accuracy, some reducts are needed for the classification. Then, control of variables as attributes are important for the classification. To select better reducts for the classification, a greedy algorithm is developed here for the classification, which is based on the selection of useful attributes These proposed methods are verified to be effective in the classification on benchmark datasets from the Reuters 21578 data set.