Decision algorithms: a survey of rough set-theoretic methods
Fundamenta Informaticae - Special issue: intelligent information systems
Reduct Generation and Classification of Gene Expression Data
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 01
Control of variables in reducts - kNN classification with confidence
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part IV
Classification by instance-based learning algorithm
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
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Dimensional reduction of data is still important as in the data processing and on the web to represent and manipulate higher dimensional data. Rough set concept developed is fundamental and useful to process higher dimensional data. Reduct in the rough set is a minimal subset of features, which has almost the same discernible power as the entire features in the higher dimensional scheme. But, we have problems of the application of reducts for the classification. Here, we develop a method which connects reducts and the nearest neighbor method to classify data with higher accuracy. To improve the classification ability of reducts, we propose a new modified reduct based on reducts and its optimization method for the classification with higher accuracy. Then, it is shown that the modified reduct improves the classification accuracy, which is followed by the optimized nearest neighbor classification.