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Universal Target Learning: An Efficient and Effective Technique for Semi-Naive Bayesian Learning

by 1,2, 3, 2,4, 2,4 and 5,*
1
College of Software, Jilin University, Changchun 130012, China
2
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
3
Department of Software and Big Data, Changzhou College of Information Technology, Changzhou 213164, China
4
College of Computer Science and Technology, Jilin University, Changchun 130012, China
5
College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130012, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(8), 729; https://doi.org/10.3390/e21080729
Received: 15 June 2019 / Revised: 15 July 2019 / Accepted: 22 July 2019 / Published: 25 July 2019
(This article belongs to the Section Information Theory, Probability and Statistics)
To mitigate the negative effect of classification bias caused by overfitting, semi-naive Bayesian techniques seek to mine the implicit dependency relationships in unlabeled testing instances. By redefining some criteria from information theory, Target Learning (TL) proposes to build for each unlabeled testing instance P the Bayesian Network Classifier BNC P , which is independent and complementary to BNC T learned from training data T . In this paper, we extend TL to Universal Target Learning (UTL) to identify redundant correlations between attribute values and maximize the bits encoded in the Bayesian network in terms of log likelihood. We take the k-dependence Bayesian classifier as an example to investigate the effect of UTL on BNC P and BNC T . Our extensive experimental results on 40 UCI datasets show that UTL can help BNC improve the generalization performance. View Full-Text
Keywords: information theory; universal target learning; Bayesian network classifier information theory; universal target learning; Bayesian network classifier
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Gao, S.; Lou, H.; Wang, L.; Liu, Y.; Fan, T. Universal Target Learning: An Efficient and Effective Technique for Semi-Naive Bayesian Learning. Entropy 2019, 21, 729.

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