Decision Tree for Dynamic and Unceratin Data streams

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, we focus on uncertain data stream classification. Based on CVFDT and DTU, we propose our UCVFDT (Uncertainty-handling and Concept-adapting Very Fast Decision Tree) algorithm, which not only maintains the ability of CVFDT to cope with concept drift with high speed, but also adds the ability to handle data with uncertain attribute. Experimental study shows that the proposed UCVFDT algorithm is efficient in classifying dynamic data stream with uncertain numerical attribute and it is computationally efficient.