Serial Number : http://www.riss.kr/link?id=A105410073

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

Author : 김해천(Hae-Cheon KIM)  ,이재성(Jae-Sung LEE)

Journal : 韓國컴퓨터情報學會論文誌(Journal of the Korea society of computer and information)

Vol. : 23 No.6 [2018]

Pages : 1-7

Published by : 한국컴퓨터정보학회

Date : 2018.06

Register Information : KCI

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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.

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