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Stochastic optimization for bayesian network classifiers

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Abstract

How to reduce the complexity of network topology and make the learned joint probability distribution fit data are two important but inconsistent issues for learning Bayesian network classifier (BNC). By transforming one single high-order topology into a set of low-order ones, ensemble learning algorithms can include more hypothesis implicated in training data and help achieve the tradeoff between bias and variance. Resampling from training data can vary the results of member classifiers of the ensemble, whereas the potentially lost information may bias the estimate of conditional probability distribution and then introduce insignificant rather than significant dependency relationships into the network topology of BNC. In this paper, we propose to learn from training data as a whole and apply heuristic search strategy to flexibly identify the significant conditional dependencies, and then the attribute order is determined implicitly. Random sampling is introduced to make each member of the ensemble “unstable” and fully represent the conditional dependencies. The experimental evaluation on 40 UCI datasets reveals that the proposed algorithm, called random Bayesian forest (RBF), achieves remarkable classification performance compared to the extended version of state-of-the-art out-of-core BNCs (e.g., SKDB, WATAN, WAODE, SA2DE, SASA2DE and IWAODE).

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Notes

  1. https://archive.ics.uci.edu/ml/datasets/MAGIC+Gamma+Telescope

  2. https://archive.ics.uci.edu/ml/datasets/Localization+Data+for+Person+Activity

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

    Yi Ren & JunYang Wei

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

    LiMin Wang & XiongFei Li

  3. College of Computer Science and Technology, Jilin University, ChangChun, 130012, China

    Meng Pang

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  1. Yi Ren
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  2. LiMin Wang
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Correspondence to LiMin Wang.

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Appendix

Appendix

Table 9 Experimental results of RMSE
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Table 10 Experimental results of bias
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Table 11 Experimental results of variance
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Ren, Y., Wang, L., Li, X. et al. Stochastic optimization for bayesian network classifiers. Appl Intell 52, 15496–15516 (2022). https://doi.org/10.1007/s10489-022-03356-z

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