Title :
Classification on nonlinear mapping of reducts based on nearest neighbor relation
Author :
Ishii, Naohiro ; Torii, Ippei ; Mukai, Naoto ; Iwata, Kazunori ; Nakashima, Toyoshiro
Author_Institution :
Dept. of Inf. Sci., Aichi Inst. of Technol., Toyota, Japan
fDate :
June 28 2015-July 1 2015
Abstract :
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. 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.
Keywords :
data mining; pattern classification; rough set theory; data manipulation; data ordering; data processing; dimension reduction; nearest neighbor relation; nonlinear mapping method; reduct generation method; reducts nonlinear mapping classification; rough set; vector data relation; Absorption; Accuracy; Data analysis; Information science; Matrix decomposition; Testing; classification; nearest neighbor relation with minimal distance; nonlinear mapping; reduct generation;
Conference_Titel :
Computer and Information Science (ICIS), 2015 IEEE/ACIS 14th International Conference on
Conference_Location :
Las Vegas, NV
DOI :
10.1109/ICIS.2015.7166642