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Open AccessArticle

General and Local: Averaged k-Dependence Bayesian Classifiers

1
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, ChangChun 130012, China
2
School of Software, Jilin University, ChangChun 130012, China
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Author to whom correspondence should be addressed.
Academic Editors: Carlos Alberto de Bragança Pereira and Adriano Polpo
Entropy 2015, 17(6), 4134-4154; https://doi.org/10.3390/e17064134
Received: 4 May 2015 / Revised: 2 June 2015 / Accepted: 9 June 2015 / Published: 16 June 2015
(This article belongs to the Special Issue Inductive Statistical Methods)
The inference of a general Bayesian network has been shown to be an NP-hard problem, even for approximate solutions. Although k-dependence Bayesian (KDB) classifier can construct at arbitrary points (values of k) along the attribute dependence spectrum, it cannot identify the changes of interdependencies when attributes take different values. Local KDB, which learns in the framework of KDB, is proposed in this study to describe the local dependencies implicated in each test instance. Based on the analysis of functional dependencies, substitution-elimination resolution, a new type of semi-naive Bayesian operation, is proposed to substitute or eliminate generalization to achieve accurate estimation of conditional probability distribution while reducing computational complexity. The final classifier, averaged k-dependence Bayesian (AKDB) classifiers, will average the output of KDB and local KDB. Experimental results on the repository of machine learning databases from the University of California Irvine (UCI) showed that AKDB has significant advantages in zero-one loss and bias relative to naive Bayes (NB), tree augmented naive Bayes (TAN), Averaged one-dependence estimators (AODE), and KDB. Moreover, KDB and local KDB show mutually complementary characteristics with respect to variance. View Full-Text
Keywords: k-dependence Bayesian classifier; substitution-elimination resolution; functionaldependency rules of probability k-dependence Bayesian classifier; substitution-elimination resolution; functionaldependency rules of probability
MDPI and ACS Style

Wang, L.; Zhao, H.; Sun, M.; Ning, Y. General and Local: Averaged k-Dependence Bayesian Classifiers. Entropy 2015, 17, 4134-4154.

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