期刊
PATTERN RECOGNITION
卷 52, 期 -, 页码 33-45出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2015.10.014
关键词
Decision tree; Active learning; Evidential likelihood; Uncertain data; Belief functions
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.
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