Nonlinear Mapping of Reducts - Nearest Neighbor Classification
Naohiro Ishii, Ippei Torii, Naoto Mukai, KazunoriIwata, Toyoshiro Nakashima · 2015
Dimension reduction of data is an important theme in the data processing. Reduct in the rough set is useful which has the same discernible power as the entire features in the higher dimensional scheme. But, classification with higher accuracy is not obtained in the reduct followed by nearest neighbor processing. To attack the problem, it is shown that nearest neighbor relation with minimal distance introduced here has a basic information for classification. In this paper, a new reduct generation method based on the nearest neighbor relation with minimal distance is proposed. To improve the classification accuracy of reducts, we develop a nonlinear mapping method on the nearest neighbor relation, which makes vector data relation among neighbor data and preserves data ordering.