Generation and Mapping of Multi-Reducts Based on Nearest Neighbor Relation

Dimension reduction of data is an important theme in the data processing to represent and manipulate higher dimensional data. Rough set 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. Combination of multi-reducts is effective for parallel processing of the classification. Nearest neighbor relation between different classes has a basic information for classification. We propose here a multi-reduct parallel processing classification scheme with efficient and higher accuracy by using nearest neighbor relation. To improve the classification ability of reducts, we develop a generation method of reducts and its graph mapping method by using the nearest neighbor relation, which is based on characteristics of the weighted reducts for the classification. Further, a dependency relation and an embedding of nearest neighbor relation are proposed to improve the classification accuracy