The Greedy Prepend Algorithm for Decision List Induction

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.

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