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ref: work-0 tags: Cohen Singer SLIPPER machine learning hypothesis generation date: 10-25-2009 18:42 gmt revision:2 [1] [0] [head]

http://www.cs.cmu.edu/~wcohen/slipper/

  • "One disadvantage of boosting is that improvements in accuracy are often obtained at the expense of comprehensibility.
  • SLIPPER = simple learner with iterative pruning to produce error reduction.
  • Inner loop: the weak lerner splits the training data, grows a single rule using one subset of the data, and then prunes the rule using the other subset.
  • They use a confidence-rated prediction based boosting algorithm, which allows the algorithm to abstain from examples not covered by the rule.
    • the sign of h(x) - the weak learner's hyposthesis - is interpreted as the predited label and the magnitude |h(x)| is the confidence in the prediction.
  • SLIPPER only handles two-class problems now, but can be extended..
  • Is better than, though not dramatically so, than c5rules (a commercial version of Quinlan's decision tree algorithms).
  • see also the excellent overview at http://www.cs.princeton.edu/~schapire/uncompress-papers.cgi/msri.ps