Abstract
People from a variety of industrial domains are beginning to realise that appropriate use of machine learning techniques for their data mining projects could bring great benefits. End-users now have to face the new problem of how to choose a combination of data processing tools and algorithms for a given dataset. This problem is usually termed the Full Model Selection (FMS) problem. Extended from our previous work [10], in this paper, we introduce a framework for designing FMS algorithms. Under this framework, we propose a novel algorithm combining both genetic algorithms (GA) and particle swarm optimization (PSO) named GPS (which stands for GA-PSO-FMS), in which a GA is used for searching the optimal structure for a data mining solution, and PSO is used for searching optimal parameters for a particular structure instance. Given a classification dataset, GPS outputs a FMS solution as a directed acyclic graph consisting of diverse data mining operators that are available to the problem. Experimental results demonstrate the benefit of the algorithm. We also present, with detailed analysis, two model-tree-based variants for speeding up the GPS algorithm.
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
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Int. Res. 16, 321–357 (2002)
Escalante, H.J., Montes, M., Sucar, L.E.: Particle swarm model selection. Journal of Machine Learning Research 10, 405–440 (2009)
Freund, Y., Schapire, R.: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, pp. 148–156 (1996)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The weka data mining software: An update. SIGKDD Explorations 11(1) (2009)
Holland, J.H.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge (1992)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995)
Landwehr, N., Hall, M., Frank, E.: Logistic model trees. Mach. Learn. 59, 161–205 (2005)
Momma, M., Bennett, K.P.: A pattern search method for model selection of support vector regression. In: Proceedings of the SIAM International Conference on Data Mining. SIAM (2002)
Sun, Q., Pfahringer, B., Mayo, M.: Full model selection in the space of data mining operators. In: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference Companion, pp. 1503–1504. ACM (2012)
Wang, Y., Witten, I.H.: Induction of model trees for predicting continuous classes. Poster Papers of the 9th European Conference on Machine Learning. Springer (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sun, Q., Pfahringer, B., Mayo, M. (2013). Towards a Framework for Designing Full Model Selection and Optimization Systems. In: Zhou, ZH., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2013. Lecture Notes in Computer Science, vol 7872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38067-9_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-38067-9_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38066-2
Online ISBN: 978-3-642-38067-9
eBook Packages: Computer ScienceComputer Science (R0)