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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