Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications 7(1), 39–59 (1994), http://portal.acm.org/citation.cfm?id=196108.196115
Bian, S., Wang, W.: On diversity and accuracy of homogeneous and heterogeneous ensembles. Int. J. Hybrid Intell. Syst. 4, 103–128 (2007), http://portal.acm.org/citation.cfm?id=1367006.1367010
Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001), http://dx.doi.org/10.1023/A:1010933404324
Bruland, T., Aamodt, A., Langseth, H.: Architectures integrating case-based reasoning and bayesian networks for clinical decision support. In: Shi, Z., Vadera, S., Aamodt, A., Leake, D. (eds.) IIP 2010. IFIP Advances in Information and Communication Technology, vol. 340, pp. 82–91. Springer, Heidelberg (2010)
Corchado, J.M., Lees, B., Aiken, J.: Hybrid instance-based system for predicting ocean temperatures. International Journal of Computational Intelligence and Applications, 35–52 (2001)
Hendrickx, I., van den Bosch, A.: Hybrid algorithms with instance-based classification. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 158–169. Springer, Heidelberg (2005)
Houeland, T.G.: An efficient random decision tree algorithm for case-based reasoning systems. In: FLAIRS Conference. AAAI Press, Menlo Park ( to appear 2011)
Houeland, T.G., Aamodt, A.: The utility problem for lazy learners - towards a non-eager approach. In: Bichindaritz, I., Montani, S. (eds.) ICCBR 2010. LNCS, vol. 6176, pp. 141–155. Springer, Heidelberg (2010), http://dx.doi.org/10.1007/978-3-642-14274-1 , doi:10.1007/978-3-642-14274-1
Knudsen, A., Aass, N., Fainsinger, R., Caraceni, A., Klepstad, P., Jordhy, M., Hjermstad, M., Kaasa, S.: Classification of pain in cancer patients a systematic literature review. Palliative Medicine 23(4), 295–308 (2009), http://pmj.sagepub.com/content/23/4/295.abstract
Minton, S.: Quantitative results concerning the utility of explanation-based learning. Artif. Intell. 42(2-3), 363–391 (1990)
Mohebpour, M.R., Adznan, B.J., Saripan, M.I.: Grid Base Classifier in Comparison to Nonparametric Methods in Multiclass Classification. Pertanika J. Sci. & Technol. 18(1), 139–154 (2010)
Policastro, C., Delbem, A., Mattoso, L., Minatti, E., Ferreira, E., Borato, C., Zanus, M.: A Hybrid Case Based Reasoning Approach for Wine Classification. In: ISDA, pp. 395–400 (2007)
Smyth, B., Cunningham, P.: The utility problem analysed - a case-based reasoning perspective. In: Proceedings of the Third European Workshop on Case-Based Reasoning, pp. 392–399. Springer, Heidelberg (1996)
Veloso, A., Meira, Jr., W.: Eager, lazy and hybrid algorithms for multi-criteria associative classification. In: Proceedings of the Data Mining Algorithms Workshop, Uberlandia, MG (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Houeland, T.G., Aamodt, A. (2011). An Efficient Hybrid Classification Algorithm – An Example from Palliative Care. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21222-2_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-21222-2_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21221-5
Online ISBN: 978-3-642-21222-2
eBook Packages: Computer ScienceComputer Science (R0)