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An Efficient Hybrid Classification Algorithm – An Example from Palliative Care

2011; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-642-21222-2_24

1611-3349

Tor Gunnar Houeland, Agnar Aamodt,

Bayesian Modeling and Causal Inference

In this paper we present an efficient hybrid classification algorithm 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 resulting classification accuracy to other similar algorithms. The hybrid algorithm consistently produces a lower average error than the base algorithms it combines, but at a higher computational cost.