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Summary: An Efficient Hybrid Classification Algorithm
- an Example from Palliative Care
Tor Gunnar Houeland, Agnar Aamodt
Department of Computer and Information Science,
Norwegian University of Science and Technology,
NO-7491 Trondheim, Norway
{houeland,agnar}@idi.ntnu.no
Abstract. In this paper we present an efficient hybrid classification al-
gorithm based on combining case-based reasoning and random decision
trees, which is based on a general approach for combining lazy and eager
learning methods. We use this hybrid classification algorithm to predict
the pain classification for palliative care patients, and compare the re-
sulting classification accuracy to other similar algorithms. The hybrid
algorithm consistently produces a lower average error than the base al-
gorithms it combines, but at a higher computational cost.
Keywords: hybrid reasoning systems, classifier combination, case-based
reasoning, random decision trees
1 Introduction
Case-based reasoning (CBR), including instance-based methods, represents a
unique approach to learning and problem solving compared to generalization-
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