Cost-sensitive decision tree for uncertain data

Abstract

Uncertainty exists widely in real-word applications. Recently, the research for uncertain data has attracted more and more attention. While not enough attention has been paid to the research of cost- sensitive algorithm on uncertain data. In this paper, we propose a simple but effective method to extend traditional cost-sensitive decision tree to uncertain data, and the algorithm can deal with both certain and uncertain data. In our experiment, we compare the proposed algorithm with DTU[18] on UCI datasets. The experimental result proves that the proposed algorithm performs better than DTU, with lower computational complexity. It keeps low cost even at high level of uncertainty, which makes it applicable to real-life applications for data uncertainty. © 2011 Springer-Verlag.