Exploiting Parallelism in Decision Tree Induction. In Parallel and Distributed computing for Machine Learning

Abstract. In the fields of data mining and machine learning the amount of data available for building classifiers is growing very fast. Parallelism may be a good solution to reduce the amount of time spent in building classifiers from very-large datasets while keeping the classification accu-racy. This work first overviews some strategies for implementing decision tree construction algorithms in parallel based on techniques such as task parallelism, data parallelism and hybrid parallelism. We then describe a new parallel implementation of the C4.5 decision tree construction algo-rithm using a breadth-first strategy, data and hybrid parallelism tech-niques. A novel contribution of this work is the ability to deal with miss-ing values. Even though minor adjustments have still to be performed to the algorithm, we present some experimental results that can be used to predict its expected behaviour of the algorithm.