基于模型似然的超1依赖贝叶斯分类器集成方法<sup>*</sup>

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Abstract Averaged onedependence estimators (AODE) is an important Bayesian learning method. However, in AODE all outputs of the superparentonedependence estimators (SPODEs) are equally considered, which may bring bad influences to the final results. In this paper, every SPODE is viewed as a generative model, and the model likelihood is used to measure its performance. Then, a new approach, named modellikelihood based superparentone dependence estimator (LODE), is proposed which integrates the SPODEs based on model likelihood. Compared with AODE, LODE significantly improves the classification performance with only a slight increase in computation.

Key words： Machine Learning Data Mining Bayesian Learning Nave Bayes Ensemble Learning

Received: 06 March 2006

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	LI Nan
	JIANG Yuan
	ZHOU ZhiHua

Cite this article:

LI Nan,JIANG Yuan,ZHOU ZhiHua. ModelLikelihood Based SuperParentOneDependence Estimator Ensemble Method[J]. , 2007, 20(6): 727-731.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2007/V20/I6/727

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