A Novel One-dependence Estimator Based on Multi-parents

Authors:
Dan Zeng;Sifa Zhang;Zhihua Cai;Siwei Jiang;Liangxiao Jiang
Affiliations:
China University of Geosciences, China;China University of Geosciences, China;China University of Geosciences, China;China University of Geosciences, China;China University of Geosciences, China
Venue:
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
Year:
2006

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To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators

IEEE Transactions on Knowledge and Data Engineering

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Abstract

Numerous approaches have been proposed to improve the classification accuracy of Naive Bayes by weakening the attribute independence assumption. To maintain the simple structure and low computational cost, many researches focus on the one-dependence estimator. In this paper, we present a novel algorithm called one-dependence estimator based on multiparents (MPODE). In MPODE, each attribute has multi-parents; we use the mean dependence probability between the attribute and its parents as one dependence estimator. Experimentally testing on the whole 36 UCI datasets recommended by Weka [1], we compare our algorithm with NB, C4.5 [2], SBC [3], TAN [4] and AODE [5]. The result shows that our algorithm outperforms NB, C4.5, SBC, and TAN significantly, and is almost same to AODE in term of classification accuracy.