Maximum margin decision surfaces for increased generalisation in evolutionary decision tree learning

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Title: Maximum margin decision surfaces for increased generalisation in evolutionary decision tree learning
Authors: Agapitos, Alexandros
O'Neill, Michael
Brabazon, Anthony
Theodoridis, Theodoros
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Date: 27-Apr-2011
Abstract: Decision tree learning is one of the most widely used and practical methods for inductive inference. We present a novel method that increases the generalisation of genetically-induced classification trees, which employ linear discriminants as the partitioning function at each internal node. Genetic Programming is employed to search the space of oblique decision trees. At the end of the evolutionary run, a (1+1) Evolution Strategy is used to geometrically optimise the boundaries in the decision space, which are represented by the linear discriminant functions. The evolutionary optimisation concerns maximising the decision-surface margin that is defined to be the smallest distance between the decision-surface and any of the samples. Initial empirical results of the application of our method to a series of datasets from the UCI repository suggest that model generalisation benefits from the margin maximisation, and that the new method is a very competent approach to pattern classification as compared to other learning algorithms.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Springer
Copyright (published version): 2011 Springer Verlag Berlin Heidelberg
Keywords: Genetic programmingGPDecision treeDT
Subject LCSH: Genetic programming (Computer science)
Decision trees
DOI: 10.1007/978-3-642-20407-4_6
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Language: en
Status of Item: Peer reviewed
Is part of: Silva, S. et al. (eds.). Genetic Programming : 14th European Conference, EuroGP 2011, Torino, Italy, April 27-29, 2011. Proceedings
Conference Details: Presented at EuroGP 2011 the 14th European Conference on Genetic Programming, Torino, Italy, April 2011
ISBN: 978-3-642-20406-7 2012-02-06T17:13:25Z
Appears in Collections:FMC² Research Collection

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