Deniz Yuret and Michael de la Maza. In Proceedings of the 21st International Symposium on Computer and Information Sciences (ISCIS 2006). LNCS 4263, Springer-Verlag
Download a C implementation of the GPA algorithm with a Weka interface here, presentation slides are here, the paper is here.
Abstract: We describe a new decision list induction algorithm called the Greedy Prepend Algorithm (GPA). GPA improves on other decision list algorithms by introducing a new objective function for rule selection and a set of novel search algorithms that allow application to large scale real world problems. GPA achieves state-of-the-art classification accuracy on the protein secondary structure prediction problem in bioinformatics and the English part of speech tagging problem in computational linguistics. For both domains GPA produces a rule set that human experts find easy to interpret, a marked advantage in decision support environments. In addition, we compare GPA to other decision list induction algorithms as well as support vector machines, C4.5, naive Bayes, and a nearest neighbor method on a number of standard data sets from the UCI machine learning repository.