Abstract
Abstract This paper describes a genetic learning system called SIA, which learns rules from a set of preclassified examples where many attribute values are missing or have “don't care” or undefined values. SIA was motivated by a data analysis task in the French justice domain and by the fact that the general machine learning methods used to deal with unknown values are not appropriate for this task. SIA is somewhat similar to the AQ algorithm because it takes an example as a seed and generalizes it (here, using a genetic process) to find a rule maximizing a rule evaluation criterion. Two mechanisms are used to deal efficiently with unknown values: the dropping of the unknown attributes in the seed example and a restricted matching operator that prevents matching with unknown values. SIA is comparable to AQ and other algorithms on two standard learning tasks and can help understanding of the French justice domain.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
