LAUSR

The expressivity of machine learning algorithms is considered to be critical in intelligent data analysis tasks for practical application. As an alternative set of classification rule learning algorithms to conventional decision tree, Prism family of algorithms induce modular rules concisely, thus exhibiting good expressiveness for human users. However, existing Prism rule induction techniques are limited by the assumption of Gaussian distribution for quantitative attributes, and may not be available for real life data analyzing, in which skewness is commonly observed. For this reason, we investigate a skew-adaptive mechanism for rule term boundary delimitation in Prism inductive learning. The propose algorithm, called P2-Prism, could learn expressive classification rules directly from quantitative data beyond Gaussian distribution. By employing statistical inference characteristics of Poisson process, our mechanism provides a significant contribution to classification rule inductive learning with adaption of skewed data distribution. The experimental evaluation of our algorithm demonstrates its skew-adaptive superiority on benchmark datasets, comparing with state-of-the-art algorithms. Furthermore, it is shown that P2-Prism is a robust classifier in the presence of various levels of noise, which further reveals its adaptability to the skewness of data distribution.