Online active learning of decision trees with evidential data

Liyao Ma (1) , Sébastien Destercke (2, 3) , Yong Wang (1)

1 USTC - University of Science and Technology of China [Hefei]
2 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]
3 Labex MS2T - Laboratoire d'Excellence "Maîtrise des Systèmes de Systèmes Technologiques"

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.

Mots clés: decision tree; active learning; uncertain data; evidential likelihood; belief functions

Domaines: Autre [cs.OH]

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