Abstract
In this paper, an ensemble classifier, namely RotaSVM, is proposed that uses recently developed rotational feature selection approach and Support Vector Machine classifier cohesively. The RotaSVM generates the number of predefined outputs of Support Vector Machines. For each Support Vector Machine, the training data is generated by splitting the feature set randomly into \(\mathcal{S}\) subsets. Subsequently, principal component analysis is used for each subset to create new feature sets and all the principal components are retained to preserve the variability information in the training data. Thereafter, such features are used to train a Support Vector Machine. During the testing phase of RotaSVM, first the rotation specific Support Vector Machines are used to test and then average posterior probability is computed to classify sample data. The effectiveness of the RotaSVM is demonstrated quantitatively by comparing it with other widely used ensemble based classifiers such as Bagging, AdaBoost, MultiBoost and Rotation Forest for 10 real-life data sets. Finally, a statistical test has been conducted to establish the superiority of the result produced by proposed RotaSVM.
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References
Benediktsson, J.A., Kittler, J., Roli, F. (eds.): MCS 2009. LNCS, vol. 5519. Springer, Heidelberg (2009)
Haindl, M., Kittler, J., Roli, F. (eds.): MCS 2007. LNCS, vol. 4472. Springer, Heidelberg (2007)
Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds.): MCS 2005. LNCS, vol. 3541. Springer, Heidelberg (2005)
Roli, F., Kittler, J., Windeatt, T. (eds.): MCS 2004. LNCS, vol. 3077. Springer, Heidelberg (2004)
Windeatt, T., Roli, F. (eds.): MCS 2003. LNCS, vol. 2709. Springer, Heidelberg (2003)
Roli, F., Kittler, J. (eds.): MCS 2002. LNCS, vol. 2364. Springer, Heidelberg (2002)
Kittler, J., Roli, F. (eds.): MCS 2001. LNCS, vol. 2096. Springer, Heidelberg (2001)
Kittler, J., Roli, F. (eds.): MCS 2000. LNCS, vol. 1857. Springer, Heidelberg (2000)
Maulik, U., Bandyopadhyay, S., Saha, I.: Integrating clustering and supervised learning for categorical data analysis. IEEE Transactions on Systems, Man and Cybernetics, Part A 40(4), 664–675 (2010)
Saha, I., Maulik, U., Bandyopadhyay, S., Plewczynski, D.: Unsupervised and supervised learning approaches together for microarray analysis. Fundamenta Informaticae 106(1), 45–73 (2011)
Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proceedings of the Thirteenth International Conference on Machine Learning, pp. 148–156 (1996)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Rodriguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation Forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(10), 1619–1630 (2006)
Giacinto, G., Roli, F.: An approach to the automatic design of multiple classifier systems. Pattern Recognition Letters 22(1), 25–33 (2001)
Perdisci, R., Ariu, D., Fogla, P., Giacinto, G., Lee, W.: McPAD: A multiple classifier system for accurate payload-based anomaly detection. Computer Networks 53, 864–881 (2009)
Giacinto, G., Perdisci, R., Rio, M.D., Roli, F.: Intrusion detection in computer networks by a modular ensemble of one-class classifiers. Information Fusion 9(1), 69–82 (2008)
Giacinto, G., Roli, F., Didaci, L.: Fusion of multiple classifiers for intrusion detection in computer networks. Pattern Recognition Letters 24(12), 1795–1803 (2003)
Saha, I., Maulik, U., Bandyopadhyay, S., Plewczynski, D.: SVMeFC: SVM ensemble fuzzy clustering for satellite image segmentation. IEEE Geoscience and Remote Sensing Letters 9(1), 52–55 (2011)
Krogh, A., Vedelsby, J.: Neural network ensembles, cross validation, and active learning. In: Advances in Neural Information Processing Systems 7, pp. 231–238 (1995)
Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152 (1992)
Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/mlrepository.html
Ferguson, G.A., Takane, Y.: Statistical analysis in psychology and education (2005)
Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)
Goh, K.S., Chang, E.Y., Li, B.: Using one-class and two-class SVMs for multiclass image annotation. IEEE Transactions on Knowledge and Data Engineering 17(10), 1333–1346 (2005)
Juang, C.F., Chiu, S.H., Shiu, S.J.: Fuzzy system learned through fuzzy clustering and support vector machine for human skin color segmentation. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 37(6), 1077–1087 (2007)
Cohen, J.A.: Coefficient of agreement for nominal scales. Educational and Psychological Measurement 20(1), 37–46 (1960)
Marom, N.D., Rokach, L., Shmilovici, A.: Using the confusion matrix for improving ensemble classifiers. In: Proceedings of the IEEE Twenty Sixth Convention of Electrical and Electronics Engineers, pp. 555–559 (2010)
Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2001)
Saha, I., Maulik, U., Plewczynski, D.: A new multi-objective technique for differential fuzzy clustering. Applied Soft Computing 11(2), 2765–2776 (2011)
Maulik, U., Saha, I.: Modified differential evolution based fuzzy clustering for pixel classification in remote sensing imagery. Pattern Recognition 42(9), 2035–2149 (2009)
Maulik, U., Saha, I.: Automatic fuzzy clustering using modified differential evolution for image classification. IEEE Transactions on Geoscience and Remote Sensing 48(9), 3503–3510 (2010)
Saha, I., Maulik, U., Bandyopadhyay, S., Plewczynski, D.: Improvement of new automatic differential fuzzy clustering using svm classifier for microarray analysis. Expert Systems with Applications 38(12), 15122–15133 (2011)
Saha, I., Plewczynski, D., Maulik, U., Bandyopadhyay, S.: Improved differential evolution for microarray analysis. International Journal of Data Mining and Bioinformatics 6(1), 86–103 (2012)
Plewczynski, D., Basu, S., Saha, I.: AMS 4.0: Consensus prediction of post-translational modifications in protein sequences. Amino Acids 43(2), 573–582 (2012)
Saha, I., Maulik, U., Bandyopadhyay, S., Plewczynski, D.: Fuzzy clustering of physicochemical and biochemical properties of amino acids. Amino Acids 43(2), 583–594 (2012)
Saha, I., Mazzocco, G., Plewczynski, D.: Consensus classification of human leukocyte antigens class II proteins. Immunogenetics 65, 97–105 (2013)
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Bhowmick, S.S., Saha, I., Rato, L., Bhattacharjee, D. (2013). RotaSVM: A New Ensemble Classifier. In: Emmerich, M., et al. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV. Advances in Intelligent Systems and Computing, vol 227. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-01128-8_4
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DOI: https://doi.org/10.1007/978-3-319-01128-8_4
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