Abstract
This study investigates the issue of obtaining stable ranking from the fusion of the result of multiple filtering methods. Rank aggregation is the process of performing multiple runs of feature selection and then aggregating the results into a final ranked list. However, a fundamental question of is how to aggregate the individual results into a single robust ranked feature list. There are a number of available methods, ranging from simple to complex. Hence we present a new rank aggregation approach. The proposed approach is composed of two stages: in the first we evaluate he similarity and stability of single filtering methods then, in the second we aggregate the results of the stable ones. The obtained results on the Australian and German credit datasets using support vector machine and decision tree confirms that ensemble feature ranking have a major impact in the performance improvement.
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 subscriptionsReferences
Ben Brahim, A., Bouaguel, W., Limam, M.: Feature selection aggregation versus classifiers aggregation for several data dimensionalities. In: Proceedings of the International Conference on Control, Engineering & Information Technology (CEIT13) (2013)
Ben brahim, A., Bouaguel, W., Limam, M.: Combining feature selection and data classification using ensemble approaches: application to cancer diagnosis and credit scoring. In: Francisr, T. (ed.) Case Studies in Intelligent Computing: Achievements and Trendss. CRC Press, Boca Raton (2013)
Fernandez, G.: Statistical data mining using SAS applications. In; Chapman & Hall/Crc: Data Mining and Knowledge Discovery. Taylor and Francis, Boca Raton (2010)
Forman, G.: BNS feature scaling: an improved representation over TF-IDF for SVM text classification. In: Proceedings of the 17th ACM Conference on Information and Knowledge Mining, pp. 263–270. ACM, New York, NY, USA (2008)
Rodriguez, I., Huerta, R., Elkan, C., Cruz, C.S.: Quadratic programming feature selection. J. Mach. Learn. Res. 11(4), 1491–1516 (2010)
Saeys, Y., Inza, I.N., Larrañaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)
Bouaguel, W., Bel Mufti, G., Limam, M.: A new feature selection technique applied to credit scoring data using a rank aggregation approach based on: optimization, genetic algorithm and similarity. In: Francisr, T. (ed.) Knowledge Discovery & Data Mining (KDDM) for Economic Development: Applications, Strategies and Techniques. CRC Press, Chicago (2014)
Wu, O., Zuo, H., Zhu, M., Hu, W., Gao, J., Wang, H.: Rank aggregation based text feature selection. In: Proceedings of the Web Intelligence, pp. 165–172. (2009)
Wang, C.M., Huang, W.F.: Evolutionary-based feature selection approaches with new criteria for data mining: a case study of credit approval data. Expert Syst. Appl. 36(3), 5900–5908 (2009)
Bouaguel, W., Bel Mufti, G.: An improvement direction for filter selection techniques using information theory measures and quadratic optimization. Int. J. Adv. Res. Artif. Intell. 1(5), 7–11 (2012)
Dittman, D.J., Khoshgoftaar, T.M., Wald, R., Napolitano, A.: Classification performance of rank aggregation techniques for ensemble gene selection. In: Boonthum-Denecke, C., Youngblood, G.M. (eds.) Proceedings of the International Conference of the Florida Artificial Intelligence Research Society (FLAIRS), AAAI Press, Coconut Grove (2013)
Saeys, Y., Abeel, T., Peer, Y.: Robust feature selection using ensemble feature selection techniques. In: Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases—Part II. ECML PKDD ‘08, pp. 313–325. Springer, Berlin, Heidelberg (2008)
Molina, L.C., Belanche, L., Nebot, A.: Feature selection algorithms: a survey and experimental evaluation. In: Proceedings of the IEEE International Conference on Data Mining, pp. 306–313. IEEE Computer Society (2002)
Dash, M., Liu, H.: Consistency-based search in feature selection. Artif. Intell. 151(1–2), 155–176 (2003)
Krishnaiah, P., Kanal, L.: Preface. In: Krishnaiah, P., Kanal, L. (eds.) Classification Pattern Recognition and Reduction of Dimensionality. Handbook of Statistics, vol. 2, pp. v–ix. Elsevier (1982)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(9), 1157–1182 (2003)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer Series in Statistics. Springer New York Inc, New York (2001)
Prati, R.C.: Combining feature ranking algorithms through rank aggregation. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. Brisbane, Australia, 10–15 June 2012
Kalousis, A., Prados, J., Hilario, M.: Stability of feature selection algorithms: a study on high-dimensional spaces. Knowl. Inf. Syst. 12(1), 95–116 (2007)
Pihur, V., Datta, S., Datta, S.: RankAggreg, an R package for weighted rank aggregation. BMC Bioinform. 10(1), 62–72 (2009)
Mak, M.W., Kung, S.Y.: Fusion of feature selection methods for pairwise scoring svm. Neurocomputing 71(16–18), 3104–3113 (2008)
Bouckaert, R.R., Frank, E., Hall, M., Kirkby, R., Reutemann, P., Seewald, A., Scuse, D.: Weka manual (3.7.1) (2009)
Kolde, R., Laur, S., Adler, P., Vilo, J.: Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics 28(4), 573–580 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer India
About this paper
Cite this paper
Bouaguel, W., Limam, M. (2016). A New Way for Combining Filter Feature Selection Methods. In: Nagar, A., Mohapatra, D., Chaki, N. (eds) Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics. Smart Innovation, Systems and Technologies, vol 43. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2538-6_43
Download citation
DOI: https://doi.org/10.1007/978-81-322-2538-6_43
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2537-9
Online ISBN: 978-81-322-2538-6
eBook Packages: EngineeringEngineering (R0)