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.

中文翻译：

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具有组概率的朴素贝叶斯算法

为了保护数据隐私，提出了一种新的传输群概率朴素贝叶斯算法TrGNB。TrGNB适用于源域在目标域中包含大量标记数据并且仅包含少量未标记数据组概率信息的场景。TrGNB将转移学习和群体概率信息的思想整合到朴素贝叶斯模型中，不仅提高了学习任务在目标域中的分类效果，而且还保护了数据隐私。TrGNB已在20个新闻组，Reuters-21578和电子邮件垃圾邮件数据集中进行了验证。实验结果表明，与基准算法相比，TrGNB大大提高了分类精度。