Abstract
When applying rules produced by induction from training examples to a test example, there are three possible cases that demand different actions: (i) no match; (ii) single match; and (iii) multiple match. Existing techniques for dealing with the first and third cases are exclusively based on probability estimation. However, when there are continuous attributes in the example space, and if these attributes have been discretized into intervals before induction, fuzzy interpretation of the discretized intervals at deduction time could be very valuable. This paper describes the fuzzy matching techniques implemented in the HCV (Version 2.0) software, and presents a hybrid interpretation mechanism which combines fuzzy matching with probability estimation. Experimental results of the HCV (Version 2.0) software with different interpretation techniques are provided on a number of data sets from the University of California at Irvine Repository of Machine Learning Databases.
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.
