Combining Classifiers via Discretization
Combining Classifiers via Discretization
复制标题
通过离散化组合分类器
DOI：
10.1080/01621459.1999.10474154
复制
发表时间：
1999
影响因子：
3.7
通讯作者：
M. Mojirsheibani
中科院分区：
文献类型：
--
作者：
M. Mojirsheibani
摘要我考虑了一种结合不同分类器来开发更有效的分类规则的方法。建议的组合分类器，这原来是强一致的，是相当简单的使用在真实的应用。它还表明，这种组合分类器是，（强烈）渐近，至少一样好的任何一个单独的分类。此外，如果单个分类器之一已经是贝叶斯最优的（渐近），那么组合分类器也是如此。
Abstract 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.