RULES-IS has proved comparable to existing rule generation algorithms such as C5.0, ID3 and the RULES family in many respects, particularly the size and accuracy of the rule set created.  RULES-IS has also demonstrated its ability to create rules using training data sets incorporating a proportion of unlabelled instances.