Analysis of rule sets generated by the CN2, ID3, and multiple convergence symbolic learning methods

Since symbolic learning methods develop distinctive sets of rules when given identical training data, questions arise as to the quality of the different rule sets produced. The results of this research provide techniques for comparing and analyzing rule sets. Numerous rule sets were generated using three wellknown symbolic learning methods; Quinlan’s ID3, Clark and Niblett’s CN2, and Murray’s Multiple Convergence algorithm. The analysis techniques were then applied to evaluate these sets of rums. The techniques as well as a guide for using them are presented in a concise summary following the discussion of the experimental results.