Inflating examples to obtain rules

Abstract

A new machine learning system is presented in this paper. It is called Inner and induces classification rules from a set of training examples. The process followed by this system starts with the random selection of a subset of examples that are iteratively inflated in order to cover the surroundings provided that they are inhabited by examples of the same class, thus becoming rules that will be applied by means of a partial matching mechanism. The rules so obtained can be seen as clusters of examples and represent clear evidence to support explanations about their future classifications, and may be used to build intelligent advisors. The whole algorithm can be seen as a set of elastic transformations of examples and rules, and produces concise, accurate rule sets, as is experimentally demonstrated in the final section of the paper