Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Incrementally optimized decision tree for noisy big data
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Incrementally optimized decision tree for noisy big data
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Multi-objective optimization for incremental decision tree learning
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
Performance evaluation of incremental decision tree learning under noisy data streams
International Journal of Computer Applications in Technology
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Very Fast Decision Tree (VFDT) is one of the most popular decision tree algorithms in data stream mining. The tree building process is based on the principle of the Hoeffding bound to decide on splitting nodes with sufficient data statistics at the leaf. The original version of VFDT requires a user-defined tie threshold by which a split will be forced to break to control the tree size. It is an open problem that the tree size grows tremendously with noise as continuous data stream in and the classifier's accuracy drops. In this paper, we propose a Moderated VFDT (M-VFDT), which uses an adaptive tie threshold for node splitting control by incremental computing. The tree building process is as fast as that of the original VFDT. The accuracy of M-VFDT improves significantly even under the presence of noise in the data stream. To solve the explosion of tree size, which is still an inherent problem in VFDT, we propose two lightweight pre-pruning mechanisms for stream mining (post-pruning is not appropriate here because of the streaming operation). Experiments are conducted to verify the merits of our new methods. M-VFDT with a pruning mechanism shows a better performance than the original VFDT at all times. Our contribution is a new model that can efficiently achieve a compact decision tree and good accuracy as an optimal balance in data stream mining.