SIA: A supervised inductive algorithm with genetic search for learning attributes based concepts

Abstract

This paper describes a genetic learning system called SIA, which learns attributes based rules from a set of preclassified examples. Examples may be described with a variable number of attributes, which can be numeric or symbolic, and examples may belong to several classes. SIA algorithm is somewhat similar to the AQ algorithm because it takes an example as a seed and generalizes it, using a genetic process, to find a rule maximizing a noise tolerant rule evaluation criterion. The SIA approach to supervised rule learning reduces greatly the possible rule search space when compared to the genetic Michigan and Pitt approaches. SIA is comparable to AQ and decision trees algorithms on two learning tasks. Furthermore, it has been designed for a data analysis task in a large and complex justice domain.