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Link to original content: https://doi.org/10.1007/s10489-023-05242-8
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Efficient heuristics for learning scalable Bayesian network classifier from labeled and unlabeled data

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Abstract

Naive Bayes (NB) is one of the top ten machine learning algorithms whereas its attribute independence assumption rarely holds in practice. A feasible and efficient approach to improving NB is relaxing the assumption by adding augmented edges to the restricted topology of NB. In this paper we prove theoretically that the generalized topology may be a suboptimal solution to model multivariate probability distributions if its fitness to data cannot be measured. Thus we propose to apply log-likelihood function as the scoring function, then introduce an efficient heuristic search strategy to explore high-dependence relationships, and for each iteration the learned topology will be improved to fit data better. The proposed algorithm, called log-likelihood Bayesian classifier (LLBC), can respectively learn two submodels from labeled training set and individual unlabeled testing instance, and then make them work jointly for classification in the framework of ensemble learning. Our extensive experimental evaluations on 36 benchmark datasets from the University of California at Irvine (UCI) machine learning repository reveal that, LLBC demonstrates excellent classification performance and provides a competitive approach to learn from labeled and unlabeled data.

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Data Availability and Access

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Notes

  1. The source code of LLBC can be found in https://github.com/Wangjj1129/LLBC

  2. The used datasets can be found in https://archive.ics.uci.edu/ml/datasets.html.

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Acknowledgements

This work is supported by the National Key Research and Development Program of China (No. 2019YFC1804804), Open Research Project of the Hubei Key Laboratory of Intelligent Geo-Information Processing (No. KLIGIP-2021A04), and the Scientific and Technological Developing Scheme of Jilin Province (No. 20200201281JC).

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Authors and Affiliations

  1. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, ChangChun, 130012, China

    Limin Wang

  2. College of Software, Jilin University, ChangChun, 130012, China

    Limin Wang, Junjie Wang & Lu Guo

  3. College of Instrumentation and Electrical Engineering, Jilin University, ChangChun, 130012, China

    Qilong Li

Authors
  1. Limin Wang
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  2. Junjie Wang
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  3. Lu Guo
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  4. Qilong Li
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Contributions

Limin Wang: Methodology, Supervision, Writing-review & editing, Funding acquisition. Junjie Wang: Conceptualization, Validation, Visualization, Writing-original draft. Lu Guo: Formal analysis, Project administration. Qilong Li: Software, Investigation.

Corresponding author

Correspondence to Junjie Wang.

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This study does not contain any studies with human participants or animals performed by any of the authors.

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Appendix

Appendix

Table 8 The experiment results of ZOL
Full size table
Table 9 The experiment results of bias
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Table 10 The experiment results of variance
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Table 11 The experiment results of RMSE
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Wang, L., Wang, J., Guo, L. et al. Efficient heuristics for learning scalable Bayesian network classifier from labeled and unlabeled data. Appl Intell 54, 1957–1979 (2024). https://doi.org/10.1007/s10489-023-05242-8

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