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Summary: ELEM2: A Learning System for More Accurate
Classifications
Aijun An and Nick Cercone
Department of Computer Science, University of Waterloo
Waterloo, Ontario N2L 3G1, Canada
Abstract. We present ELEM2, a new method for inducing classification
rules from a set of examples. The method employs several new strategies
in the induction and classification processes to improve the predictive
performance of induced rules. In particular, a new heuristic function for
evaluating attribute-value pairs is proposed. The function is defined to
reflect the degree of relevance of an attribute-value pair to a target con-
cept and leads to selection of the most relevant pairs for formulating
rules. Another feature of ELEM2 is that it handles inconsistent training
data by defining an unlearnable region of a concept based on the prob-
ability distribution of that concept in the training data. To further deal
with imperfect data, ELEM2 makes use of the post-pruning technique to
remove unreliable portions of a generated rule. A new rule quality mea-
sure is proposed for the purpose of post-pruning. The measure is defined
according to the relative distribution of a rule with respect to positive
and negative examples. To show whether ELEM2 achieves its objective,
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