Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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A Novel Posterior Probability Estimation Method for Multi-label Naive Bayes Classification

  • Kim, Hae-Cheon (School of Computer Science and Engineering, Chang-Ang University) ;
  • Lee, Jaesung (School of Computer Science and Engineering, Chung-Ang University)
  • Received : 2018.03.20
  • Accepted : 2018.05.28
  • Published : 2018.06.29
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Abstract

A multi-label classification is to find multiple labels associated with the input pattern. Multi-label classification can be achieved by extending conventional single-label classification. Common extension techniques are known as Binary relevance, Label powerset, and Classifier chains. However, most of the extended multi-label naive bayes classifier has not been able to accurately estimate posterior probabilities because it does not reflect the label dependency. And the remaining extended multi-label naive bayes classifier has a problem that it is unstable to estimate posterior probability according to the label selection order. To estimate posterior probability well, we propose a new posterior probability estimation method that reflects the probability between all labels and labels efficiently. The proposed method reflects the correlation between labels. And we have confirmed through experiments that the extended multi-label naive bayes classifier using the proposed method has higher accuracy then the existing multi-label naive bayes classifiers.

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References

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