计算机工程与应用 ›› 2008, Vol. 44 ›› Issue (22): 146-148.DOI: 10.3778/j.issn.1002-8331.2008.22.043
• 数据库、信号与信息处理 • 上一篇 下一篇
陈 冰,张化祥
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CHEN Bing,ZHANG Hua-xiang
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摘要： 为提高数据分类的性能，提出了一种基于信息熵[1]的多分类器动态组合方法（EMDA）。此方法在多个UCI标准数据集上进行了测试，并与由集成学习算法—AdaBoost，训练出的各个基分类器的分类效果进行比较，证明了该算法的有效性。
关键词: 多分类器, 信息熵, 聚类, 分类器组合, Adaboost
Abstract: A method of dynamic ensemble of multiple classifiers based on information entropy（EMDA） is proposed in the paper，in order to improve the classification performance of dataset.The algorithm is tested on the UCI benchmark data sets，and comparative classification efficiency with several member classfiers trained based on ensemble learning algorithm—Adaboost.In the end，the utility of EMDA algorithm can be proved in the paper.
Key words: multiple classifiers, information entropy, clustering, classifier ensemble, Adaboost
陈 冰,张化祥. 一种基于信息熵的多分类器动态组合方法[J]. 计算机工程与应用, 2008, 44(22): 146-148.
CHEN Bing,ZHANG Hua-xiang. Method of dynamic ensemble of multiple classifiers based on information entropy[J]. Computer Engineering and Applications, 2008, 44(22): 146-148.
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链接本文: http://cea.ceaj.org/CN/10.3778/j.issn.1002-8331.2008.22.043
http://cea.ceaj.org/CN/Y2008/V44/I22/146
