Abstract
Feature selection is a topic of growing interest mainly due to the increasing amount of information, being an essential task in many machine learning problems with high dimensional data. The selection of a subset of relevant features help to reduce the complexity of the problem and the building of robust learning models. This work presents an adaptation of a recent quadratic programming feature selection technique that identifies in one-fold the redundancy and relevance on data. Our approach introduces a non-probabilistic measure to capture the relevance based on Minimum Spanning Trees. Three different real datasets were used to assess the performance of the adaptation. The results are encouraging and reflect the utility of feature selection algorithms.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Balagani, K.S., Phoha, V.V.: On the feature selection criterion based on an approximation of multidimensional mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1342–1343 (2010)
Ben-David, A., Sterling, L.: Generating rules from examples of human multiattribute decision making should be simple. Expert Systems with Applications 31(2), 390–396 (2006)
Cardoso, J.S., Cardoso, M.J.: Towards an intelligent medical system for the aesthetic evaluation of breast cancer conservative treatment. Artificial Intelligence in Medicine 40, 115–126 (2007)
Estévez, P.A., Tesmer, M., Perez, C.A., Zurada, J.M.: Normalized mutual information feature selection. Trans. Neur. Netw. 20, 189–201 (2009)
Frey, P.W., Slate, D.J.: Letter recognition using holland-style adaptive classifiers. Mach. Learn. 6, 161–182 (1991)
Friedman, J.H., Rafsky, L.C.: Multivariate generalizations of the wald-wolfowitz and smirnov two-sample tests. Annals of Statistics 7(4), 697–717 (1979)
Oliveira, H.P., Magalhaes, A., Cardoso, M.J., Cardoso, J.S.: An accurate and interpretable model for bcct.core. In: Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6158–6161 (2010)
Rodriguez-Lujan, I., Huerta, R., Elkan, C., Cruz, C.S.: Quadratic programming feature selection. Journal of Machine Learning Research 11, 1491–1516 (2010)
Seth, S., Príncipe, J.C.: Variable Selection: A Statistical Dependence Perspective. In: Proceeding of the Ninth International Conference on Machine Learning and Applications, pp. 931–936 (2010)
Singh, S.: Multiresolution estimates of classification complexity. IEEE Trans. Pattern Anal. Mach. Intell. 25, 1534–1539 (2003)
Somol, P., Novovicova, J.: Evaluating stability and comparing output of feature selectors that optimize feature subset cardinality. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(11), 1921–1939 (2010)
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
Sousa, R., Oliveira, H.P., Cardoso, J.S. (2011). Feature Selection with Complexity Measure in a Quadratic Programming Setting. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds) Pattern Recognition and Image Analysis. IbPRIA 2011. Lecture Notes in Computer Science, vol 6669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21257-4_65
Download citation
DOI: https://doi.org/10.1007/978-3-642-21257-4_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21256-7
Online ISBN: 978-3-642-21257-4
eBook Packages: Computer ScienceComputer Science (R0)