The greedy prepend algorithm for decision list induction

Authors:
Deniz Yuret;Michael de la Maza
Affiliations:
Koç University, Istanbul, Turkey;Park Hudson Finance, Cambridge, MA
Venue:
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
Year:
2006

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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.