An Efficient Decision Tree Classification Method Based on Extended Hash Table for Data Streams Mining
Zhenzheng Ouyang, Quanyuan Wu, Tao Wang · 2008
This paper focuses on continuous attributes handling for mining data stream with concept drift. Data stream is an incremental, online and real time model. Domingos and Hulten have presented a one-pass algorithm. Their system VFDT use Hoeffding inequality to achieve a probabilistic bound on the accuracy of the tree constructed. VFDTpsilas extended version CVFDT handles concept drift efficiently. In this paper, we revisit this problem and implemented a system HashCVFDT on top of CVFDT. It is as fast as hash table when inserting, seeking or deleting attribute value, and it also can sort the attribute value.