International Journal of Automation, Control and Intelligent Systems
Articles Information
International Journal of Automation, Control and Intelligent Systems, Vol.1, No.2, Jul. 2015, Pub. Date: Jul. 28, 2015
A Diverse Clustering Method on Biological Big Data
Pages: 61-65 Views: 1328 Downloads: 499
Authors
[01] Mohsen Rezaei, Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad, Iran.
Abstract
In the past decade many new methods were proposed for creating diverse classifiers due to combination. In this paper a new method for constructing an ensemble is proposed which uses clustering technique to generate perturbation in training datasets. Main presumption of this method is that the clustering algorithm used can find the natural groups of data in feature space. During testing, the classifiers whose votes are considered as being reliable are combined using majority voting. This method of combination outperforms the ensemble of all classifiers considerably on several real and artificial datasets.
Keywords
Diversity, Classifier Fusion, Clustering, Classifier Ensembles
References
[01] B. Minaei-Bidgoli, G. Kortemeyer and W.F. Punch, Optimizing Classification Ensembles via a Genetic Algorithm for a Web-based Educational System, (SSPR /SPR 2004), Lecture Notes in Computer Science (LNCS), Volume 3138, Springer-Verlag, ISBN: 3-540-22570-6, pp. 397-406, 2004.
[02] Saberi., M. Vahidi, B. Minaei-Bidgoli, Learn to Detect Phishing Scams Using Learning and Ensemble Methods, IEEE/WIC/ACM International Conference on Intelligent Agent Technology, Workshops (IAT 07), pp. 311-314, Silicon Valley, USA, November 2-5, 2007.
[03] T.G. Dietterich, Ensemble learning, in The Handbook of Brain Theory and Neural Networks, 2nd edition, M.A. Arbib, Ed. Cambridge, MA: MIT Press, 2002.
[04] H. Parvin, H. Alinejad-Rokny, S. Parvin, Divide and Conquer Classification, Australian Journal of Basic & Applied Sciences, 5(12), 2446-2452 (2011).
[05] H. Parvin, B. Minaei-Bidgoli, H. Alinejad-Rokny, A New Imbalanced Learning and Dictions Tree Method for Breast Cancer Diagnosis, Journal of Bionanoscience, 7(6), 673-678 (2013).
[06] H. Parvin, H. Alinejad-Rokny, M. Asadi, An Ensemble Based Approach for Feature Selection, Journal of Applied Sciences Research, 7(9), 33-43 (2011).
[07] S. Gunter and H. Bunke, Creation of classifier ensembles for handwritten word recognition using feature selection algorithms, IWFHR 2002 on January 15, 2002.
[08] B. Minaei-Bidgoli, G. Kortemeyer, W. F. Punch, Mining Feature Importance: Applying Evolutionary Algorithms within a Web-Based Educational System, Proc. of the Int. Conf. on Cybernetics and Information Technologies, Systems and Applications, CITSA 2004.
[09] L. I. Kuncheva, Combining Pattern Classifiers, Methods and Algorithms, New York: Wiley, 2005.
[10] L. Shapley and B. Grofman, Optimizing group judgmental accuracy in the presence of interdependencies, Public Choice, 43:329-343, 1984.
[11] H. Parvin, H. Helmi, B. Minaie-Bidgoli, H. Alinejad-Rokny, H. Shirgahi, Linkage learning based on differences in local optimums of building blocks with one optima, International Journal of Physical Sciences, 6(14), 3419-3425 (2011).
[12] H. Parvin, B. Minaei-Bidgoli, H. Alinejad-Rokny, S. Ghatei, An innovative combination of particle swarm optimization, learning automaton and great deluge algorithms for dynamic environments, International Journal of Physical Sciences, 6(22), 5121-5127 (2011).
[13] H. Parvin, H. Alinejad-Rokny, S. Parvin, A Classifier Ensemble of Binary Classifier Ensembles, International Journal of Learning Management Systems, 1(2), 37-47 (2013).
[14] F. Roli and J. Kittler, editors. Proc. 2nd Int. Workshop on Multiple Classifier Systems (MCS 2001), Vol. 2096 of Lecture Notes in Computer Science LNCS Springer-Verlag, Cambridge, UK, 2001.
[15] F. Roli and J. Kittler, editors. Proc. 3rd Int. Workshop on Multiple Classifier Systems (MCS 2002), Vol. 2364 of Lecture Notes in Computer Science LNCS Springer-Verlag, Cagliari, Italy, 2002.
[16] L. Lam. Classifier combinations: implementations and theoretical issues. In J. Kittler and F. Roli, editors, Multiple Classifier Systems, Vol. 1857 of Lecture Notes in Computer Science, Cagliari, Italy, 2000, Springer, pp. 78-86.
[17] T.G. Dietrich, Machine-learning research: four current direction, AI Magazine, 18, 4, winter 1997, 97-135.
[18] H. Parvin, B. Minaei-Bidgoli, H. Alinejad-Rokny, W.F. Punch, Data weighing mechanisms for clustering ensembles, Computers & Electrical Engineering, 39(5), 1433-1450 (2013).
[19] H. Parvin, H. Alinejad-Rokny, B. Minaei-Bidgoli, S. Parvin, A new classifier ensemble methodology based on subspace learning, Journal of Experimental & Theoretical Artificial Intelligence, 25(2), 227-250 (2013).
[20] H. Parvin, H. Alinejad-Rokny, N. Seyedaghaee, S. Parvin, A Heuristic Scalable Classifier Ensemble of Binary Classifier Ensembles, Journal of Bioinformatics and Intelligent Control, 1(2), 163-170 (2013).
[21] A.K. Jain, R.P.W. Duin, J. Mao, Satanical pattern recognition: a review, IEEE Transaction on Pattern Analysis and Machine Intelligence, PAMI-22, 1, January 2000, 4-37.
[22] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. John Wiley & Sons, NY, 2001.
[23] J. C. Sharkey, editor, Combining Artificial Neural Nets. Ensemble and Modular Multi-Net Systems, Springer-Verlag, London, 1999.
[24] L. K. Hansen, P. Salamon, Neural network ensembles. IEEE Transaction on Pattern Analysis and Machine Intelligence, 12(10):993-1001, 1990.
[25] Krogh, J. Vedelsdy, Neural Network Ensembles Cross Validation, and Active Learning, In: G. Tesauro, D. Touretzky, T. Leen (Eds.), Advances in Neural Information Processing Systems, Volume 7. MIT Press, Cambridge, MA, p.231-238, 1995.
[26] L. Breiman, Bagging predictors. Machine Learning, 24(2): 123-140, 1996.
[27] R.E. Schapire, The strength of weak learn ability, Machine Learning, 5(2): 1971-227, 1990.
[28] P. Melville, R. Mooney, Constructing Diverse Classifier Ensembles Using Artificial Training Examples, Proc. of the IJCAI-2003, Acapulco, Mexico, p. 505-510, 2003.
[29] B. E. Rosen, Ensemble learning using decorrelated neural network. Connection Science, 8(3-4): 373-384, 1996.
[30] Y. Liu, X. Yao, Evolutionary ensembles with negative correlation learning, IEEE Trans. Evolutionary Computation, 4(4): 380-387, 2000.
[31] D. Opitz, J. Shavlik, Actively searching for an effective neural network ensemble, Connection Science, 8(3-4): 337-353, 1996.
[32] Parvin, H. Alinejad-Rokny, N. Seyedaghaee, S. Parvin, A Heuristic Scalable Classifier Ensemble of Binary Classifier Ensembles, Journal of Bioinformatics and Intelligent Control, 1(2), 163-170 (2013).
[33] Lazarevic, Z. Obradovic, Effective pruning of neural network classifier ensembles. Proc. International Joint Conference on Neural Networks, 2:796-801, 2001.
[34] H. D. Navone, P. F. Verdes, P. M. Granitto, H. A. Ceccatto, Selecting Diverse Members of Neural Network Ensembles, Proc. 16th Brazilian Symposium on Neural Networks, p.255-260, 2000.
[35] Z. H. Zhou, J. X. Wu, Y. Jiang, S. F. Chen, Genetic algorithm based selective neural network ensemble, Proc. 17th International Joint Conference on Artificial Intelligence, 2:797-802, 2001.
[36] M.H. Fouladgar, B. Minaei-Bidgoli, H. Parvin, H. Alinejad-Rokny, Extension in The Case of Arrays in Daikon like Tools, Advanced Engineering Technology and Application, 2(1), 5-10 (2013).
[37] H. Parvin, M. MirnabiBaboli, H. Alinejad-Rokny, Proposing a Classifier Ensemble Framework Based on Classifier Selection and Decision Tree, Engineering Applications of Artificial Intelligence, 37, 34-42 (2015).
[38] H. Parvin, B. Minaei-Bidgoli, H. Alinejad-Rokny, W.F. Punch, Data weighing mechanisms for clustering ensembles, Computers & Electrical Engineering, 39(5), 1433-1450 (2013).
[39] Q. Fu, S. X. Hu, S. Y. Zhao, A PSO-based approach for neural network ensemble, Journal of Zhejiang University (Engineering Science), 38(12):1596-1600, 2004, (in Chinese).
[40] Y. Freund, R.E. Schapire, A decision-theoretic generalization of online learning and an application to boosting, in Proceedings of the 2nd European Conference on Computational Learning Theory, Barcelona, Spain, pp.23–37, 1995.
[41] B. Efron, R. Tibshirani, An Introduction to the Bootstrap, New York: Chapman & Hall, 1993.
[42] V. Dobra, Scalable Classification And Regression Tree Construction, Ph.D. Dissertation, Cornell University, Ithaca, NY, 2003.
[43] T. G. Dietterich, Ensemble methods in machine learning. In J. Kittler and F. Roli, editors, Multiple Classifier Systems, volume 1857 of Lecture Notes in Computer Science, Springer, pp. 1–15, Cagliari, Italy, 2000.
[44] F. Roli, G. Giacinto, G. Vernazza. Methods for designing multiple classifier systems. In J. Kittler and F. Roli, editors, Proc. 2nd International Workshop on Multiple Classifier Systems, Vol. 2096 of Lecture Notes in Computer Science, Springer- Verlag, pp. 78–87, Cambridge, UK, 2001.
[45] S. Dudoit, J. Fridlyand, Bagging to improve the accuracy of a clustering procedure. Bioinformatics, 19 (9), pp. 1090-1099, 2003.
[46] L.I. Kuncheva, L.C. Jain, Designing Classifier Fusion Systems by Genetic Algorithms. IEEE Transaction on Evolutionary Computation, Vol. 33, 351-373, 2000.
[47] Strehl, J. Ghosh, Cluster ensembles a knowledge reuse framework for combining multiple partitions. Journal on Machine Learning Research, pp. 583-617, 2002.
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