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Open AccessArticle

Discriminatory Target Learning: Mining Significant Dependence Relationships from Labeled and Unlabeled Data

Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
Faculty of Science, Engineering & Built Environment, Deakin University Geelong, Burwood, VIC 3125, Australia
Changzhou College of Information Technology, Changzhou 213164, China
College of Computer Science and Technology, Jilin University, Changchun 130012, China
Author to whom correspondence should be addressed.
Entropy 2019, 21(5), 537;
Received: 27 April 2019 / Revised: 20 May 2019 / Accepted: 24 May 2019 / Published: 26 May 2019
(This article belongs to the Special Issue Information Theoretic Measures and Their Applications)
Machine learning techniques have shown superior predictive power, among which Bayesian network classifiers (BNCs) have remained of great interest due to its capacity to demonstrate complex dependence relationships. Most traditional BNCs tend to build only one model to fit training instances by analyzing independence between attributes using conditional mutual information. However, for different class labels, the conditional dependence relationships may be different rather than invariant when attributes take different values, which may result in classification bias. To address this issue, we propose a novel framework, called discriminatory target learning, which can be regarded as a tradeoff between probabilistic model learned from unlabeled instance at the uncertain end and that learned from labeled training data at the certain end. The final model can discriminately represent the dependence relationships hidden in unlabeled instance with respect to different possible class labels. Taking k-dependence Bayesian classifier as an example, experimental comparison on 42 publicly available datasets indicated that the final model achieved competitive classification performance compared to state-of-the-art learners such as Random forest and averaged one-dependence estimators. View Full-Text
Keywords: Bayesian network; discriminatory target learning; unlabeled instance Bayesian network; discriminatory target learning; unlabeled instance
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Duan, Z.-Y.; Wang, L.-M.; Mammadov, M.; Lou, H.; Sun, M.-H. Discriminatory Target Learning: Mining Significant Dependence Relationships from Labeled and Unlabeled Data. Entropy 2019, 21, 537.

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