Rule Set Quality Measures For Inductive Learning Algorithms
Ralf Klinkenberg · 1996
Symbolic inductive learning systems that induce concept descriptions from examples are valuable tools in the task of knowledge acquisition for expert systems. Since inductive learning methods produce distinct concept descriptions when given identical training data, questions arise as to the quality of the different rule sets produced. This work provides several techniques for comparing and analyzing rule sets. These techniques measure the accuracy, generalization, time and space complexity, and domain coverage of rule sets. Based on these metrics, the performance of four different inductive learning systems is compared. These systems are Michalski et al.'s AQ15 (1986a; 1986b; Hong, Mozetic, and Michalski, 1986; Wnek et al., 1995), Quinlan's C4.5 (1993), Clark and Niblett's CN2 (Clark and Niblett, 1989; Clark and Boswell, 1991), and Janikow's Genetic-based Inductive Learning system (GIL) (1991; 1993). The comparison is based on rule sets generated by these algorithms for six real world data sets. ...