Online active learning of decision trees with evidential data

PATTERN RECOGNITION

卷:52

Article

Ma, Liyao¹  Destercke, Sebastien²  Wang, Yong¹ 

[1] Univ Sci & Technol China, Dept Automat, Hefei 230026, Peoples R China

[2] Ctr Rech Royallieu, Heudiasyc UMR7253, Compiegne, France

关键词: Decision tree;    Active learning;    Evidential likelihood;    Uncertain data;    Belief functions;   

DOI  :  10.1016/j.patcog.2015.10.014

来源: Elsevier

【 摘 要 】

Learning from uncertain data has attracted increasing attention in recent years. In this paper, we propose a decision tree learning method that can not only handle uncertain data, but also reduce epistemic uncertainty by querying the most valuable uncertain instances within the learning procedure. Specifically, we use entropy intervals extracted from the evidential likelihood to query uncertain training instances when needed, with the goal to improve the selection of the splitting attribute. Experimental results under various conditions confirm the interest of the proposed approach. (C) 2015 Elsevier Ltd. All rights reserved.

【 授权许可】

Free