Rule Stacking: An Approach for Compressing an Ensemble of Rule Sets into a Single Classifier


In this paper, we present an approach for compressing a rule-based pairwise classifier ensemble into a single rule set that can be directly used for classification. The key idea is to re-encode the training examples using information about which of the original ruler covers the example, and to use them for training a rule-based meta-level classifier. We not… (More)
DOI: 10.1007/978-3-642-24477-3_26


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