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Entropy 2017, 19(9), 501; https://doi.org/10.3390/e19090501

Attribute Value Weighted Average of One-Dependence Estimators

1
School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
2
School of Mechanical Engineering and Electronic Information, Wuhan University of Engineering Science, Wuhan 430200, China
3
Department of Computer Science, China University of Geosciences, Wuhan 430074, China
4
Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Received: 8 July 2017 / Revised: 16 August 2017 / Accepted: 11 September 2017 / Published: 16 September 2017
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Abstract

Of numerous proposals to improve the accuracy of naive Bayes by weakening its attribute independence assumption, semi-naive Bayesian classifiers which utilize one-dependence estimators (ODEs) have been shown to be able to approximate the ground-truth attribute dependencies; meanwhile, the probability estimation in ODEs is effective, thus leading to excellent performance. In previous studies, ODEs were exploited directly in a simple way. For example, averaged one-dependence estimators (AODE) weaken the attribute independence assumption by directly averaging all of a constrained class of classifiers. However, all one-dependence estimators in AODE have the same weights and are treated equally. In this study, we propose a new paradigm based on a simple, efficient, and effective attribute value weighting approach, called attribute value weighted average of one-dependence estimators (AVWAODE). AVWAODE assigns discriminative weights to different ODEs by computing the correlation between the different root attribute value and the class. Our approach uses two different attribute value weighting measures: the Kullback–Leibler (KL) measure and the information gain (IG) measure, and thus two different versions are created, which are simply denoted by AVWAODE-KL and AVWAODE-IG, respectively. We experimentally tested them using a collection of 36 University of California at Irvine (UCI) datasets and found that they both achieved better performance than some other state-of-the-art Bayesian classifiers used for comparison. View Full-Text
Keywords: attribute value weighting; Kullback–Leibler measure; information gain; entropy attribute value weighting; Kullback–Leibler measure; information gain; entropy
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Yu, L.; Jiang, L.; Wang, D.; Zhang, L. Attribute Value Weighted Average of One-Dependence Estimators. Entropy 2017, 19, 501.

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