Classification by instance-based learning algorithm

Authors:
Yongguang Bao;Eisuke Tsuchiya;Naohiro Ishii;Xiaoyong Du
Affiliations:
Aichi Information System, Japan;Department of Information Networking Eng, Aichi Institute of Technology, Japan;Department of Information Networking Eng, Aichi Institute of Technology, Japan;School of Information, Renmin University of China, China
Venue:
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Year:
2005

Citing 3
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Abstract

The basic k-nearest-neighbor classification algorithm works well in many domains but has several shortcomings. This paper proposes a tolerant instance-based learning algorithm TIBL and it’s combining method by simple voting of TIBL, which is an integration of genetic algorithm, tolerant rough sets and k-nearest neighbor classification algorithm. The proposed algorithms seek to reduce storage requirement and increase generalization accuracy when compared to the basic k-nearest neighbor algorithm and other learning models. Experiments have been conducted on some benchmark datasets from the UCI Machine Learning Repository. The results show that TIBL algorithm and it’s combining method, improve the performance of the k-nearest neighbor classification, and also achieves higher generalization accuracy than other popular machine learning algorithms.