An Evidence Theory Decision Tree Algorithm for Uncertain Data
Li Fang, Yi Chen, Chong Wang · 2009
Decision trees are considered as one of the efficient classification techniques in data mining fields. But the standard decision trees are unfit to cope with data pervaded with uncertainty both at the construction and classification phase. Dempster-Shafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. This paper added new aggregation combination operator and uncertainty measure operator into general framework for data mining based on evidence theory. Combining these two operators with decision tree and multidimensional cube, decision tree technique can be extended to uncertain environment. In the phase of node splitting, this algorithm can pre-prune the decision tree and generate a decision tree with fewer branches. Simulations have shown the effectiveness of this method.