Decision Tree for Dynamic and Uncertain Data Streams
Chunquan Liang (Northwest A&F University), Yang Zhang (Northwest
A&F University), and Qun Song (Northwest A&F University);
JMLR W&P 13:209-224, 2010.
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.