Generation of reducts based on nearest neighbor relation
Naohiro Ishii, Ippei Torii, Toyoshiro Nakashima, Kazunori Iwata · 2014
Dimension reduction of data is an important theme in the data processing and on the web 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 the same discernible power as the entire features in the higher dimensional scheme. Nearest neighbor relation between different classes has a basic information for classification. We propose here a reduct generation method based on the nearest neighbor relation. To characterize the classification ability of reducts, we develop a new graph mapping method of the nearest neighbor based on reducts and weighted modified reducts for the classification with higher accuracy.