Universal Target Learning: An Efficient and Effective Technique for Semi-Naive Bayesian Learning
College of Software, Jilin University, Changchun 130012, China
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
Department of Software and Big Data, Changzhou College of Information Technology, Changzhou 213164, China
College of Computer Science and Technology, Jilin University, Changchun 130012, China
College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130012, China
Author to whom correspondence should be addressed.
Received: 15 June 2019 / Revised: 15 July 2019 / Accepted: 22 July 2019 / Published: 25 July 2019
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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
the Bayesian Network Classifier BNC
, which is independent and complementary to BNC
learned from training data
. 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
. Our extensive experimental results on 40 UCI datasets show that UTL can help BNC improve the generalization performance.
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MDPI and ACS Style
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.
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(8):729.
Gao, Siqi; Lou, Hua; Wang, Limin; Liu, Yang; Fan, Tiehu. 2019. "Universal Target Learning: An Efficient and Effective Technique for Semi-Naive Bayesian Learning." Entropy 21, no. 8: 729.
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