Multilabel naïve Bayes classification considering label dependence
- Authors
- Kim, Hae-Cheon; Park, Jin-Hyeong; Kim, Dae-Won; Lee, Jaesung
- Issue Date
- Aug-2020
- Publisher
- Elsevier B.V.
- Keywords
- Label dependence; Multilabel classifier; Naïve Bayes classification
- Citation
- Pattern Recognition Letters, v.136, pp 279 - 285
- Pages
- 7
- Journal Title
- Pattern Recognition Letters
- Volume
- 136
- Start Page
- 279
- End Page
- 285
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/52831
- DOI
- 10.1016/j.patrec.2020.06.021
- ISSN
- 0167-8655
1872-7344
- 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 naïve Bayes classifier, to utilize the useful relations among labels for achieving better multilabel classification accuracy. However, the conventional multilabel naïve Bayes classifier treats each label independently and hence neglects the relations among labels, resulting in degenerated accuracy. We propose a new multilabel naïve Bayes classifier that considers the relations or dependence among labels. Experimental results show that the proposed method outperforms conventional multilabel classifiers. © 2020 Elsevier B.V.
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Collections - College of Software > Department of Artificial Intelligence > 1. Journal Articles
- College of Software > School of Computer Science and Engineering > 1. Journal Articles
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