A Framework On: Decision Tree for Dynamic Uncertain Data

Current research on data stream classification mainly focuses on certain data, in which precise and definite value is usually assumed. However, data with uncertainty is quite natural in real-world application due to various causes, including imprecise measurement, repeated sampling and network errors. In this paper, a new approach is proposed to construct a fuzzy decision tree (FDT) when the training set is built incrementally and when training examples are provided temporally. In this paper, we focus on uncertain data stream classification. Based on DTDU, we propose our DTDU (Decision Tree for Dynamic Uncertain Data) algorithm. Experimental study shows that the proposed DTDU algorithm is efficient in classifying dynamic data stream with uncertain numerical attribute and it is computationally efficient.