Improved rule discovery performance on uncertainty

In this paper we describe the improved version of a novel rule induction algorithm, namely ILA. We first outline the basic algorithm, and then present how the algorithm is enhanced using the new evaluation metric that handles uncertainty in a given data set. In addition to having a faster induction than the original one, we believe that our contribution comes into picture with a new metric that allows users to define their preferences through a penalty factor. We use this penalty factor to tackle with over-fitting bias, which is inherently found in a great many of inductive algorithms. We compare the improved algorithm ILA-2 to a variety of induction algorithms, including ID3, OC1, C4.5, CN2, and ILA. According to our preliminary experimental work, the algorithm appears to be comparable to the well-known algorithms such as CN2 and C4.5 in terms of accuracy and size.