Hybrid Interpretation of 'No Match' and 'Multiple Match' in Induction

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 "rst 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.