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Transfer Naive Bayes algorithm with group probabilities

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Abstract

In order to protect data privacy, a new transfer group probability Naive Bayes algorithm TrGNB is proposed. TrGNB is applied to scenarios in which the source domain contains a large amount of labeled data and only a small amount of unlabeled data group probability information in the target domain. TrGNB integrates the ideology of transfer learning and group probability information into the Naive Bayes model, which not only improves the classification effect of the learning task in the target domain but also protects the data privacy. The TrGNB was verified on the 20-Newsgroups, Reuters-21578 and Email spam datasets. The experimental results show that TrGNB significantly improves the classification accuracy compared with the benchmark algorithms.

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Acknowledgements

This work was supported by the National Key Research and Development Plan of China under Grant No. 2016YFB0801004.

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Authors and Affiliations

  1. College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, People’s Republic of China

    Jingmei Li, Weifei Wu & Di Xue

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  1. Jingmei Li
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  2. Weifei Wu
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  3. Di Xue
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Correspondence to Weifei Wu.

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Li, J., Wu, W. & Xue, D. Transfer Naive Bayes algorithm with group probabilities. Appl Intell 50, 61–73 (2020). https://doi.org/10.1007/s10489-019-01512-6

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