Abstract
In this paper, ensemble methods for different base classifiers are proposed. An ensemble technique is a supervised learning algorithm that combines a group of classifiers in order to acquire an overall model with more exact decisions. The classifiers that are support vector machine (SVM), naive Bayes (NB), and back propagation neural network (BPNN) are trained and tested on different gene expression datasets using both random selection method and k-fold cross-validation method. Both binary-class and multi-class datasets are used for evaluation of effectiveness of the ensemble method. Various publicly available gene expression datasets have been used for experiments in order to find the accuracy and effectiveness of the ensemble technique. Performance of the different classification methods and ensemble methods has been compared by using the accuracy values. The results have shown that the accuracy for the gene expression datasets has been increased by using the ensemble methods.
This is a preview of subscription content, log in via an institution to check access.
References
Kun, M. 2013. A Vision-Based Hybrid Method for Eye Detection and Tracking. International Journal of Security and Its Applications.
Rokach, L. 2010. Ensemble Methods in Supervised Learning, vol. 33, 1–33. Springer.
Rokach, L. 2005. Ensemble Methods for Classifiers. Data Mining and Knowledge Discovery Handbook, Springer, US, 957–980.
Enriquez, F., F.L. Cruz, F. Javier Ortega, C.G. Vallego, and J.A. Troyano. 2013. A Comparative Study of Combination Applied to NLP Tasks. Information Fusion 14: 255–267.
Zhan, G.P. 2000. Neural Networks for Classification: A Survey. IEEE Transactions on Systems, Man and Cybernetics-Part. C: Applications and Reviews 30 (4): 451–446.
Ziadduin, S., and M.N. Dailey. 2008. Iris Recognition Performance Enhancement Using Weighted Majority Voting. 15th IEEE Inter-National Conference on Image Processing, 227–280.
Isa, S.M., M. Ivan Fanany, W. Jatmiko, and A. Murni Arymurthy. 2011. Sleep Apnea Detection from ECG Signal: Analysis on Optimal Features. In Principal Components and Nonlinearity, 5th International Conference on Bioinformatics and Biomedical Engineering.
Kittler, J., M. Hatef, R.P.W. Duin, and J. Matas. 1998. On Combining Classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 2 (3): 226–239.
Kim, Seoyoung, and Y. Kim. 2012. Application-Specific Cloud Provisioning Model Using Job Profiles Analysis. In IEEE 14th Conference on High Performance Computing and Communication and IEEE 9th International Conference on Embedded Software and Systems.
Luo, L., E.F. Wood, and M. Pan. 2007. Bayesian Merging of Multiple Climate Model Forecasts for Seasonal Hydrological Predictions. Journal of Geophysical Research 112: 1–13.
Ajitha, P., and G. Gunasekaran. 2014. Semantic Based Intuitive Topic Search Engine. International Review on Computers and Software.
Chen, Z., J. Li, L. Wei, W. Xu, and Y. Shi. 2011. Multiple-Kernel SVM Based Multiple-Task Oriented Data Mining System for Gene Expression Data Analysis. Expert Systems with Applications 38: 12151–12159.
Hansen, L., and P. Salamon. 1990. Neural Network Ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12: 993–1001.
Rokach. 2014. Decision Forests, Series in Machine Perception and Artificial Intelligence.
Helman, Paul, Robert Vero Susan, R. Atlas, and Cheryl Will-man. 2004. A Bayesian Network Classification Methodology for Gene Expression Data. Journal of Computational Biology 11 (4): 581–615.
Tsiliki, G., and S. Kossida. 2011. Fusion Methodologies for Biomedical Data. Journal of Proteomics 74: 2774–2785.
Kapp, M.N., R. Sabourin, and P. Maupin. 2012. A Dynamic Model Selection Strategy for Support Vector Machine Classifiers. Applied Soft Computing 12 (8): 2550–2565.
Dzeroski, S., and B. Zenko. 2004. Is Combining Classiers with Stacking Better Than Selecting the Best One? Machine Learning 54: 255–273.
Hong, Zi, and Jing-vu Yang. 1991. Optimal Discriminant Plane for a Small Number of Samples and Design Method of Classifier on the Plane. Pattern Recognition 24 (4): 317–324.
http://archieve.ics.uci.edu/ml/datasets/yeast+dataset,1997-06-06.
Seeja, K.R., and Shweta. 2011. Microarray Data Classification Using Support Vector Machine. International Journal on Biometric and Bioinformatics 5 (1): 10–15.
Shah, C., and A.G. Jivani. 2013. Comparison of Data Mining Classification Algorithms for Breast Cancer Prediction. In Proceedings. 4th International Conference on Computing, Communication and Net-working Technologies, 1–4.
ReboiroJato, M., F. Diaz, D. Glez-Pena, and F. Fdez-Riverola. 2014. A Novel Ensemble of Classifiers That Use Biological Relevant Gene Sets for Micro-array Classification. Applied Soft Computing 17: 117–126.
Opitz, D., and R. Maclin. 1999. Popular Ensemble Methods: An Empirical Study. Journal of Artificial Intelligence Research 11: 169–198.
Morrison, D., and L.C. De Silva. 2007. Voting Assembles of Spoken a ECT Classification. Journal of Network and Computer Applications 30: 1356–1365.
AliBagheri, M., Q. Gao, and S. Escalera. 2013. Logo Recognition Based on the Dempster-Shafer Fusion of Multiple Classifiers. Advances in Artificial Intelligence Lecture Notes in Computer Science 7884: 1–12.
Sohn, S.Y., and S. Ho Lee. 2003. Data Fusion, Ensemble and Clustering to Improve the Classification Accuracy for the Severity of Road Track Accidents in Korea. Safety Science 41: 1–14.
Hanczar, B., and A. BarHen. 2012. A New Measure of Classifier Performance for Gene Expression Data. IEEE/ACM Transactions on Computational Biology and Bioinformatics 9 (5): 1379–1386.
Tong, M., K. Hong Liu, C. Xu, and W. Ju. 2013. An Ensemble of SVM Classifiers Based on Gene Pairs. Computers in Biology and Medicine 43: 729–737.
Liu, H., L. Liu, and H. Zhang. 2010. Ensemble Gene Selection by Grouping for Microarray Data Classification. Journal of Biomedical Informatics 43: 81–87.
Reboiro-Jato, M., F. Diaz, D. Glez-Pena, and F. Fdez-Riverola. 2014. A Novel Ensemble of Classifiers That Use Biological Relevant Gene Sets for Microarray Classification. Applied Soft Computing 17: 117–126.
Nanni, L., and A. Lumini. 2007. Ensemblator: An Ensemble of Classifiers for Reliable Classification of Biological Data. Pattern Recognition Letters 28 (5): 622–630.
Lee, J., M. Park, and S. Song. 2005. An Extensive Comparison of Recent Classification Tools Applied to Microarray Data. Computational Statistics and Data Analysis 48 (4): 869–885.
Boulesteix, A., C. Strobl, T. Augustin, and M. Daumer. 2008. Evaluating Microarray Based Classifiers: An Overview. Cancer Informatics 6: 77–97.
Xu, L., A. Krzyzak and C.Y. Suen. 1992. Methods of Combining Multiple Classifiers and Their Applications to Handwriting Recognition. IEEE Transactions on Systems, Man and Cybernetics 22 (3): 418–435.
Chen, M.S., J. Han, and P.S. Yu. 1996. Data Mining: An Overview from a Database Perspective. IEEE Transactions on Knowledge and Data Engineering 8: 866–883.
Han, J., and M. Kamber. 2001. Data Mining, Concepts and Techniques, 67–120. Morgann Kaufmann Publishers.
Ester, M., H.P. Kriegel, J. Sander, and X. Xu. 1996. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining, vol. 96, 226–231.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Panda, M., Mishra, D., Mishra, S. (2018). Ensemble Methods for Improving Classifier Performance. In: Reddy, M., Viswanath, K., K.M., S. (eds) International Proceedings on Advances in Soft Computing, Intelligent Systems and Applications . Advances in Intelligent Systems and Computing, vol 628. Springer, Singapore. https://doi.org/10.1007/978-981-10-5272-9_34
Download citation
DOI: https://doi.org/10.1007/978-981-10-5272-9_34
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-5271-2
Online ISBN: 978-981-10-5272-9
eBook Packages: EngineeringEngineering (R0)