A streaming parallel decision tree algorithm

We propose a new algorithm for building decision tree classifiers. The algorithm is executed in a distributed environment and is especially designed for classifying large data sets and streaming data. It is empirically shown to be as accurate as a standard decision tree classifier, while being scalable for processing of streaming data on multiple processors. These findings are supported by a rigorous analysis of the algorithm's accuracy. The essence of the algorithm is to quickly construct histograms at the processors, which compress the data to a fixed amount of memory. A master processor uses this information to find near-optimal split points to terminal tree nodes. Our analysis shows that guarantees on the local accuracy of split points imply guarantees on the overall tree accuracy. © 2010 Yael Ben-Haim and Elad Tom-Tov.