Multilabel naïve Bayes classification considering label dependence

Abstract Multilabel classification is the task of assigning relevant labels to an instance, and it has received considerable attention in recent years. This task can be performed by extending a single-label classifier, such as the naive Bayes classifier, to utilize the useful relations among labels for achieving better multilabel classification accuracy. However, the conventional multilabel naive Bayes classifier treats each label independently and hence neglects the relations among labels, resulting in degenerated accuracy. We propose a new multilabel naive Bayes classifier that considers the relations or dependence among labels. Experimental results show that the proposed method outperforms conventional multilabel classifiers.

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