A Novel Posterior Probability Estimation Method for Multi-label Naive Bayes Classification (다중 레이블 나이브 베이즈 분류를 위한 새로운 사후확률 추정 방법에 관한 연구)

Hae Choen Kim (김해천); Jaesung Lee (이재성)

doi:10.9708/jksci.2018.23.06.001

A Novel Posterior Probability Estimation Method for Multi-label Naive Bayes Classification

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2018, 23(6), pp.1-7
DOI : 10.9708/jksci.2018.23.06.001
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : March 20, 2018
Accepted : May 28, 2018
Published : June 29, 2018

Hae Choen Kim ¹, Jaesung Lee ¹

¹중앙대학교

Accredited

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.

KEYWORDS

Multi-label Classification, Naive Bayes Classifier, Posterior Probability Estimation, Label Dependency

Citation status

* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

A Novel Posterior Probability Estimation Method for Multi-label Naive Bayes Classification

ABSTRACT

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

A Novel Posterior Probability Estimation Method for Multi-label Naive Bayes Classification

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.