Ila-2: an Inductive Learning Algorithm for Knowledge Discovery

In this paper we describe the ILA-2 rule induction algorithm which is the improved version of a novel inductive learning algorithm, ILA. We first outline the basic algorithm ILA, and then present how the algorithm is improved using a new evaluation metric that handles uncertainty in the data. By using a new soft computing metric, users can reflect their preferences through a penalty factor to control the performance of the algorithm. ILA has also a faster pass criteria feature which reduces the processing time without sacrificing much from the accuracy that is not available in basic ILA. We experimentally show that the performance of ILA-2 is comparable to that of well-known inductive learning algorithms, namely CN2, OC1, ID3 and C4.5.