Abstract
Ensemble methods are preferred as they represent good significance over specific predictor regarding accuracy and confidence in classification. This paper proposes here the ensemble method with multiple independent feature subsets in order to classify high-dimensional data in the area of the biomedicine using Correlation feature selection with Stratified Sampling and Radial Basis Functions Neural Network. First, the method selects the feature subsets using Correlation-based feature Selection with Stratified Sampling. It minimizes the redundancy in the features. After generating the feature subsets, each feature subset is trained using base classifier and then these results are combined using majority voting. The proposed method uses CFS-SS in ensemble classification method.
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
References
Yongjun Piao, Hyun Woo Park, Cheng Hao Ji, Keun Ho Ryu, “Ensemble Method for Classification of High-Dimensional Data”, 978-1-4799-3919-0/14/ IEEE Big Comp 2014.
Rahman, B. Verma,”Ensemble Classifier Generation using Non uniform Layered Clustering and Genetic Algorithm”, Knowledge-Based System, 2013, in press.
Sung-Bae Cho, Hong-HeeWon, “Cancer classification using ensemble of neural networks with multiple significant gene subsets”, published online: 12 November 2006 Springer Science+Business Media, LLC 2007.
Yongjun Piao, Minghao Piao, Kiejung Park and Keun Ho Ryu, “An Ensemble Correlation-Based Gene Selection Algorithm for Cancer Classification with Gene Expression Data”, Bioinformatics Advance Access published October 11, 2012.
L. Breiman, “Bagging predictors”, Mach. Learning. 24, 1996, pp. 123–140.
Y. Freund, R.E. Schapire, “Experiments with a new boosting algorithm”, In the Proceeding of the Thirteenth International Conference on Machine Learning, 1996, pp. 148–156.
Leo Breiman, “Random Forests”, Springer Journal, Machine Learning, 45, 5–32, 2001.
Sung-Bae Cho, Hong-Hee Won, “Cancer classification using ensemble of neural networks with multiple significant gene subsets”, published online: 12 November 2006 Springer Science & Business Media, LLC 2007.
Hanaa Ismail Elshazly; Abeer Mohamed Elkorany, Aboul Ella Hassanien, “Ensemble-based classifiers for prostate cancer diagnosis”, 9th International Computer Engineering Conference Faculty of Engineering, Cairo University Giza, EGYPT December 28–29, 2013.
Josef Kittler, Mohamad Hatef, Robert P.W. Duin, and Jiri Matas, “On Combining Classifiers” IEEE Transactions on, “Pattern Analysis and Machine Intelligence”, Vol. 20, NO. 3 March 1998.
M. A. Hall, “Correlation based Feature Selection (CFS) for Discrete and Numeric Class Machine Learning”, Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29 - July 2, 2000.
Jacek Biesiada, Wodzisaw Duch, “A Kolmogorov-Smirnov Correlation-Based Filter for Microarray Data”, 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13–16, 2007.
Guyon I, Weston J, Barnhill S, Vapnik V. “Gene selection for cancer classification using support vector machines”, Machine Learning. 2002, 46:389–422.
Xinguo Lu, Xianghua Peng, Ping Liu, Yong Deng, Bingtao Feng, Bo Liao, “A Novel Feature Selection Method Based on CFS in Cancer Recognition”, Systems Biology (ISB), 2012 IEEE 6th International Conference on 18–20 Aug. 2012, 226 – 231.
J. Dhande, D.R. Dandekar, S.L. Badjate, “Performance Improvement of Ann Classifiers using Pso”. In Proceedings IASTED VIIP Conference, pages 733–738, 2003.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media Singapore
About this paper
Cite this paper
Meshram, S.B., Shinde, S.M. (2017). Ensemble Method Using Correlation-Based Feature Selection with Stratified Sampling for Classification. In: Satapathy, S., Bhateja, V., Joshi, A. (eds) Proceedings of the International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 468. Springer, Singapore. https://doi.org/10.1007/978-981-10-1675-2_6
Download citation
DOI: https://doi.org/10.1007/978-981-10-1675-2_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-1674-5
Online ISBN: 978-981-10-1675-2
eBook Packages: EngineeringEngineering (R0)