Advances in Inductive Rule Learning

A extension to the k-nearest neighbor classifier is described in which automatically induced rules are used as binary features, which are active in an instance when the left-hand side of the corresponding rule matches with the instance. The ripper rule induction algorithm is employed to produce the rules. The similarity between a memory instance and a new instance is based on the rules the two instances share. We report on experiments that indicate that (i) the method equals the generalization performances of ripper and k-NN classification on average, and (ii) when the original multi-valued features are combined with the transformed rule-based features, some significant improvements in k-NN classification are observed, particularly with artificial benchmark tasks.