document: Accurate decision trees for mining high-speed data streams In this paper we study the problem of constructing accurate decision tree models from data streams . Data streams are incremental tasks that require incremental , online , and any-time learning algorithms . One of the most successful algorithms for mining data streams is VFDT . In this paper we extend the VFDT system in two directions : the ability to deal with continuous data and the use of more powerful classification techniques at tree leaves . The proposed system , VFDTc , can incorporate and classify new information online , with a single scan of the data , in time constant per example . The most relevant property of our system is the ability to obtain a performance similar to a standard decision tree algorithm even for medium size datasets . This is relevant due to the any-time property . We study the behavior of VFDTc in different problems and demonstrate its utility in large and medium data sets . Under a bias-variance analysis we observe that VFDTc in comparison to C4 .5 is able to reduce the variance component .
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