Improving performance of the k-nearest neighbor classifier by tolerant rough sets

Author

Bao, Yongguang ; Du, Xiaoyong ; Ishii, Naohiro

Author_Institution

Dept.of Intelligence and Comput. Sci., Nagoya Inst. of Technol., Japan

fYear

2001

fDate

2001

Firstpage

167

Lastpage

171

Abstract

The authors report on efforts to improve the performance of k-nearest neighbor classification by introducing the tolerant rough set. We relate the tolerant rough relation with object similarity. Two objects are called similar if and only if these two objects satisfy the requirements of the tolerant rough relation. Hence, the tolerant rough set is used to select objects from the training data and constructing the similarity function. A genetic algorithm (GA) algorithm is used for seeking optimal similarity metrics. Experiments have been conducted on some artificial and real world data, and the results show that our algorithm can improve the performance of the k-nearest neighbor classification, and achieve a higher accuracy compared with the C4.5 system

Keywords

data mining; genetic algorithms; learning (artificial intelligence); pattern classification; rough set theory; GA algorithm; data mining; genetic algorithm; k-nearest neighbor classification; object similarity; optimal similarity metrics; similarity function; tolerant rough relation; tolerant rough set; training data; Application software; Association rules; Computer science; Computer vision; Data mining; Nearest neighbor searches; Pattern classification; Pattern recognition; Rough sets; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Cooperative Database Systems for Advanced Applications, 2001. CODAS 2001. The Proceedings of the Third International Symposium on

Conference_Location

Beijing

Print_ISBN

0-7695-1128-7

Type

conf

DOI

10.1109/CODAS.2001.945163

Filename

945163