Discriminatory target learning: mining significant dependence relationships from labeled and unlabeled data

Duan, Zhi-Yi, Wang, Li-Min, Mammadov, Musa, Lou, Hua and Sun, Ming-Hui 2019, Discriminatory target learning: mining significant dependence relationships from labeled and unlabeled data, Entropy, vol. 21, no. 5, doi: 10.3390/e21050537.

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Title Discriminatory target learning: mining significant dependence relationships from labeled and unlabeled data
Author(s) Duan, Zhi-Yi
Wang, Li-Min
Mammadov, Musa
Lou, Hua
Sun, Ming-Hui
Journal name Entropy
Volume number 21
Issue number 5
Total pages 26
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2019-05
ISSN 1099-4300
1099-4300
Summary © 2019 by the authors. 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.
Language eng
DOI 10.3390/e21050537
Field of Research 01 Mathematical Sciences
02 Physical Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2019, The Authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30122711

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