Multi-Label Classification With Bayes Theorem

摘要：

Compared with single-label classification, multilabel classification is more general in practice, since it allows one instance to have more than one label simultaneously. Bayes Theorem has been successfully applied to deal with single-label classification. In this paper, we proposed to tackle multi-label classification using Bayes Theorem. We propose two approaches, coined as Pair-Dependency Multi-Label Bayesian Classifier (PDMLBC) and Complete-Dependency Multi-Label Bayesian Classifier (CDMLBC). PDMLBC takes advantage of label dependency between any two labels, while CDMLBC considers the dependency among a set of labels. In the experiments, we evaluate the performance of PDMLBC and CDMLBC on real medical data, the results show that both PDMLBC and CDMLBC methods outperform NB+BR on all metrics, and CDMLBC works best among the three methods.

作者: Hui Wu Guangzhi Qu Hui Zhang Craig T. Hartrick

作者单位: Computer Science and Engineering Department, Oakland University Rochester, MI, 48309 USA State Key Laboratory of Software Development Environment School of Computer Science, Beihang Univers Anesthesiology Research, School of Medicine, Oakland University Rochester, MI, 48309 USA

会议类型: 国际会议

会议名称: 2011 Eighth International Conference on Fuzzy System and Knowledge Discovery(第八届模糊系统与知识发现国际会议 FSKD 2011)

会议地点: 上海

会议语种:英文

页码: 1083-1087

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）