NDLI: Combining classification improvements by ensemble processing

Content Provider	IEEE Xplore Digital Library
Author	Ishii, N. Tsuchiya, E. Bao, Y. Yamaguchi, N.
Copyright Year	2005
Description	Author affiliation: Aichi Inst. of Technol., Toyota, Japan (Ishii, N.; Tsuchiya, E.)
Abstract	The k-nearest neighbor (KNN) classification is a simple and effective classification approach. However, improving performance of the classifier is still attractive. Combining multiple classifiers is an effective technique for improving accuracy. There are many general combining algorithms, such as Bagging, Boosting, or Error Correcting Output Coding that significantly improve the classifier such as decision trees, rule learners, or neural networks. Unfortunately, these combining methods developed do not improve the nearest neighbor classifiers. In this paper, first, we present a new approach to combine multiple KNN classifiers based on different distance functions, in which we apply multiple distance functions to improve the performance of the k-nearest neighbor classifier. Second, we develop a combining method, in which the weights of the distance function are learnt by genetic algorithm. Finally, combining classifiers in error correcting output coding, are discussed. The proposed algorithms seek to increase generalization accuracy when compared to the basic k-nearest neighbor algorithm. Experiments have been conducted on some benchmark datasets from the UCI machine learning repository. The results show that the proposed algorithms improve the performance of the k-nearest neighbor classification.
Sponsorship	Int. Assoc. for Comput. and Inf. Sci. (ACIS)
Starting Page	240
Ending Page	246
File Size	568924
Page Count	7
File Format	PDF
ISBN	0769522971
DOI	10.1109/SERA.2005.30
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning algorithms Error correction Bagging Boosting Classification tree analysis Decision trees Neural networks Nearest neighbor searches Genetic algorithms Machine learning Knowledge Discovery Data Mining Artificial Intelligence
Content Type	Text
Resource Type	Article

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