Online active learning of decision trees with evidential data

Abstract : Learning from uncertain data has been drawing increasing attention in recent years. In this paper, we propose a tree induction approach which can not only handle uncertain data, but also furthermore reduce epistemic uncertainty by querying the most valuable uncertain instances within the learning procedure. We extend classical decision trees to the framework of belief functions to deal with a variety of uncertainties in the data. In particular, we use entropy intervals extracted from the evidential likelihood to query selected uncertain querying training instances when needed, in order to improve the selection of the splitting attribute. Our experiments show the good performances of proposed active belief decision trees under different conditions.
Document type :
Journal articles
Complete list of metadatas

Cited literature [31 references]  Display  Hide  Download
Contributor : Sébastien Destercke <>
Submitted on : Tuesday, January 12, 2016 - 9:36:13 AM
Last modification on : Tuesday, July 24, 2018 - 4:40:02 PM
Long-term archiving on : Thursday, November 10, 2016 - 11:44:53 PM


active belief decision tree.pd...
Files produced by the author(s)




Liyao Ma, Sébastien Destercke, Yong Wang. Online active learning of decision trees with evidential data. Pattern Recognition, Elsevier, 2016, 52, pp.33-45. ⟨10.1016/j.patcog.2015.10.014⟩. ⟨hal-01254290⟩



Record views


Files downloads