DocumentCode :
2251645
Title :
Modified Reduct: Nearest Neighbor Classification
Author :
Ishii, Naohiro ; Torii, Ippei ; Bao, Yongguang ; Tanaka, Hidekazu
Author_Institution :
Dept. of Inf. Sci., Aichi Inst. of Technol., Toyota, Japan
fYear :
2012
fDate :
May 30 2012-June 1 2012
Firstpage :
310
Lastpage :
315
Abstract :
Dimension reduction of data is an important theme as in the data processing and on the web to represent and manipulate higher dimensional data. Rough set developed 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. Then, there are relations between reducts and their classification classes. Here, we develop a method which connects reducts and the nearest neighbor method to classify data with higher classification accuracy. To improve the classification ability of reducts, we propose a new modified reduct and its optimization method for the classification with higher accuracy. Then, it is shown that the modified reduct improves the classification accuracy, which is followed by the optimized nearest neighbor classification.
Keywords :
Internet; data reduction; data structures; pattern classification; rough set theory; Web; data classification; data dimension reduction; data processing; dimensional data manipulation; dimensional data representation; modified reduct; nearest neighbor classification; optimization method; rough sets theory; Accuracy; Classification algorithms; Euclidean distance; Information science; Information systems; Reliability; Training; classification; dimension reduction; nearest neighbor classification; reduct;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer and Information Science (ICIS), 2012 IEEE/ACIS 11th International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4673-1536-4
Type :
conf
DOI :
10.1109/ICIS.2012.72
Filename :
6211115
Link To Document :
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