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Improved Naive Bayes with Mislabeled Data

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arxiv 2304.06292 v1 pith:BMTVK3KF submitted 2023-04-13 cs.LG stat.APstat.ME

Improved Naive Bayes with Mislabeled Data

Qianhan Zeng , Yingqiu Zhu , Xuening Zhu , Feifei Wang , Weichen Zhao , Shuning Sun , Meng Su , Hansheng Wang This is my paper
classification cs.LG stat.APstat.ME
keywords bayesnaiveimprovedmethodclassificationdataincorrectlabeling
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Labeling mistakes are frequently encountered in real-world applications. If not treated well, the labeling mistakes can deteriorate the classification performances of a model seriously. To address this issue, we propose an improved Naive Bayes method for text classification. It is analytically simple and free of subjective judgements on the correct and incorrect labels. By specifying the generating mechanism of incorrect labels, we optimize the corresponding log-likelihood function iteratively by using an EM algorithm. Our simulation and experiment results show that the improved Naive Bayes method greatly improves the performances of the Naive Bayes method with mislabeled data.

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