Abstract
We address the problem of identifying a non-redundant subset of important variables. All modern feature selection approaches including filters, wrappers, and embedded methods experience problems in very general settings with massive mixed-type data, and with complex relationships between the inputs and the target. We propose an efficient ensemble-based approach measuring statistical independence between a target and a potentially very large number of inputs including any meaningful order of interactions between them, removing redundancies from the relevant ones, and finally ranking variables in the identified minimum feature set. Experiments with synthetic data illustrate the sensitivity and the selectivity of the method, whereas the scalability of the method is demonstrated with a real car sensor data base.
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
Borisov, A., Eruhimov, V., Tuv, E.: Dynamic soft feature selection for tree-based ensembles. In: Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L. (eds.) Feature Extraction, Foundations and Applications. Springer, New York (2005)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. CRC Press, Boca Raton (1984)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Technical report, Dept. of Statistics, Stanford University (1999)
Gabrilovich, E., Markovitch, S.: Text categorization with many redundant features: Using aggressive feature selection to make svms competitive with c4.5. In: Proc. ICML 2004 (2004)
Ho, T.K.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)
Torkkola, K., Tuv, E.: Ensembles of regularized least squares classifiers for highdimensional problems. In: Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L. (eds.) Feature Extraction, Foundations and Applications. Springer, Heidelberg (2005)
Torkkola, K., Gardner, M., Wood, C., Schreiner, C., Massey, N., Leivian, B., Summers, J., Venkatesan, S.: Toward modeling and classification of naturalistic driving. In: Proceedings of the 2005 IEEE Intelligent Vehicles Symposium, Las Vegas, NV, USA, June 6 - 8 2005, pp. 638–643 (2005)
Tuv, E.: Feature selection and ensemble learning. In: Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L. (eds.) Feature Extraction, Foundations and Applications. Springer, New York (2005)
Tuv, E., Borisov, A., Torkkola, K.: Feature selection using ensemble based ranking against artificial contrasts. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2006), Vancouver, Canada, July 16-22 (2006)
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
Tuv, E., Borisov, A., Torkkola, K. (2006). Best Subset Feature Selection for Massive Mixed-Type Problems. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_125
Download citation
DOI: https://doi.org/10.1007/11875581_125
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
Online ISBN: 978-3-540-45487-8
eBook Packages: Computer ScienceComputer Science (R0)