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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.