Parallel streaming decision trees

author: Yossi Richter, IBM Haifa Research Lab
published: Sept. 1, 2008,   recorded: July 2008,   views: 4246


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A new algorithm for building decision tree classifiers is proposed. The algorithm is executed in a distributed environment and is especially designed for classifying large datasets and streaming data. It is empirically shown to be as accurate as standard decision tree classifiers, while being scalable to infinite streaming data and multiple processors.

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