Classification by Partil Data of Multiple Reducts-kNN with Confidence

Most classification studies are done by using all the objective data. It is expected to classify objects by using some subsets data effectively. A rough set based reduct is a minimal subset of features, which has almost the same discernible power as the entire features. Here, we propose multiple reducts which are followed by the k-nearest neighbor with confidence to classify documents with higher 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.