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Combining Classifiers via Discretization

 

作者: Majid Mojirsheibani,  

 

期刊: Journal of the American Statistical Association  (Taylor Available online 1999)
卷期: Volume 94, issue 446  

页码: 600-609

 

ISSN:0162-1459

 

年代: 1999

 

DOI:10.1080/01621459.1999.10474154

 

出版商: Taylor & Francis Group

 

关键词: Bayes classifier;Combined classifier;Consistency;Misclassification error

 

数据来源: Taylor

 

摘要:

I consider a method for combining different classifiers to develop more effective classification rules. The proposed combined classifier, which turns out to be strongly consistent, is quite simple to use in real applications. It is also shown that this combined classifier is, (strongly) asymptotically, at least as good as any one of the individual classifiers. In addition, if one of the individual classifiers is already Bayes optimal (asymptotically), then so is the combined classifier.

 

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