Scaling Up: Distributed Machine Learning with Cooperation

  • Daniel Hennessy
  • Foster Provost

Machine-learning methods are becoming increasingly popular for automated data analysis.  However, standard methods do not scale up to massive scientific and business data sets without expensive hardware.  This paper investigates a practical alternative for scaling up: the use of distributed processing to take advantage of the often dormant PCs and workstations available on local networks.  Each workstation runs a common rule-learning program on a subset of the data.  We first show that for commonly used rule evaluation criteria, a simple form of cooperation can guarantee that a rule will look good to the set of cooperating learners if and only if it would look good to a single learner operating with the entire data set.  We then show how such a system can further capitalize on different perspectives by sharing learned knowledge for significant reduction in search effort.  We demonstrate the power of the method by learning from a massive data set taken from the domain of cellular fraud detection.  Finally, we provide an overview of other methods for scaling up machine learning.

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