Incremental decision tree based on order statistics

New application domains generate data which are not persistent anymore but volatile: network management, web profile modeling... These data arrive quickly, massively and are visible just once. Thus they necessarily have to be learnt according to their arrival orders. For classification problems online decision trees are known to perform well and are widely used on streaming data. In this paper, we propose a new decision tree method based on order statistics. The construction of an online tree usually needs summaries in the leaves. Our solution uses bounded error quantiles summaries. A robust and performing discretization or grouping method uses these summaries to provide, at the same time, a criterion to find the best split and better density estimations. This estimation is then used to build a näıve Bayes classifier in the leaves to improve the prediction in the early learning stage.