Abstract
In data mining, hybrid intelligent systems present a synergistic combination of multiple approaches to develop the next generation of intelligent systems. Our paper presents an integration of a Combined Multiple Models (CMM) technique with an evolutionary approach that is used for tuning of parameters. Proposed hybrid classifier was tested in microarray analysis domain. This domain was chosen intentionally, because of the nature of Combined Multiple Models classifiers that are specialized in solving problems with high dimensionality and contain low number of samples. Evolutionary tuning of parameters in combination with validation dataset enables fine tuning of parameters that are usually set to pre-defined values. Using this technique we made another step in leveling the accuracy of comprehensible classifiers to those represented by ensembles of classifiers.
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
Domingos, P.: Knowledge acquisition from examples via multiple models. In: Proc. of the 14th International Conference on Machine Learning, pp. 98–106. Morgan Kaufmann, San Francisco (1997)
Domingos, P.: Knowledge Discovery Via Multiple Models. Intelligent Data Analysis 2(1-4), 187–202 (1998)
Estruch, V., Ferri, C., Hernández-Orallo, J., RamÃrez-Quintana, M.J.: Simple Mimetic Classifiers. In: Proc. IAPR International Conference on Machine Learning and Data Mining (MLDM 2003), pp. 156–171 (2003)
Cohn, D., Atlas, L., Ladner, R.: Improving generalization with active learning. Machine Learning 15, 201–221 (1994)
Craven, M.W., Shavlik, J.W.: Extracting comprehensible concept representations from trained neural networks. In: Working Notes on the IJCAI 1995 Workshop on Comprehensibility in Machine Learning, Montreal, Canada, pp. 61–75 (1995)
Schwefel, H.P.: Kybernetische Evolution als Strategie der exprimentellen Forschung in der Strömungstechnik, Master’s thesis, Technical University of Berlin (1965)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Li, J., Liu, H.: Ensembles of cascading trees. In: Proc. IEEE International Conference on Data Mining, p. 585. IEEE Computer Society, Melbourne (2003)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools with Java implementations. Morgan Kaufmann, San Francisco (1999)
Freund, Y., Schapire, R.E.: Experiments with a New Boosting Algorithm. In: Proc. of the Thirteenth International Conference on Machine Learning, pp. 148–156. Morgan Kaufmann, San Francisco (1996)
Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.S.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286(5439), 531–537 (1999)
van Veer, L.J., Dai, H., van De Vijver, M.J., He, Y.D., Hart, A.A., Mao, M., Peterse, H.L., Kooy, K.D., Marton, M.J., Witteveen, A.T., Schreiber, G.J., Kerkhoven, R.M., Roberts, C., Linsley, P.S., Bernards, R., Friend, S.H.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002)
Gordon, G.J., Jensen, R.V., Hsiao, L.-L., Gullans, S.R., Blumenstock, J.E., Ramaswami, S., Richards, W.G., Sugarbaker, D.J., Bueno, R.: Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. Cancer Research 62, 4963–4967 (2002)
Armstrong, S.A., Staunton, J.E., Silverman, L.B., Pieters, R., den Boer, M.L., Minden, M.D., Sallan, S.E., Lander, E.S., Golub, T.R., Korsmeyer, S.J.: MLL translocations specify a distinct gene expression profile that distinguishes a unique leukaemia. Nat. Genet. 30(1), 41–47 (2002)
Singh, D., et al.: Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1, 203–209 (2002)
Stiglic, G., Kokol, P.: Knowledge extraction from microarray datasets using combined multiple models. In: Proc. IEEE International Conference on Data Mining Workshop on Foundation of semantic oriented data and web mining, Houston, Texas, pp. 75–80 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Stiglic, G., Kokol, P. (2006). Evolutionary Tuning of Combined Multiple Models. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893004_164
Download citation
DOI: https://doi.org/10.1007/11893004_164
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46537-9
Online ISBN: 978-3-540-46539-3
eBook Packages: Computer ScienceComputer Science (R0)