Ensemble Classifiers and Their Applications: A Review
Akhlaqur Rahman , Sumaira Tasnim."Ensemble Classifiers and Their Applications: A Review". International Journal of Computer Trends and Technology (IJCTT) V10(1):31-35, April 2014. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
Ensemble classifier refers to a group of individual classifiers that are cooperatively trained on data set in a supervised classification problem. In this paper we present a review of commonly used ensemble classifiers in the literature. Some ensemble classifiers are also developed targeting specific applications. We also present some application driven ensemble classifiers in this paper.
References
[1] R. Polikar, Ensemble based systems in decision making, IEEE Circuits and Systems Magazine, 6 (3) (2006), pp. 21–45
[2] E.K. Tang, P.N. Suganthan, X. Yao, An analysis of diversity measures, Machine Learning, 65 (2006), pp. 247–271
[3] G. Brown, J.L. Wyatt, R. Harris, X. Yao, Diversity creation methods: a survey and categorization, Information Fusion, 6 (1) (2005), pp. 5–20
[4] L.I. Kuncheva, C.J. Whitaker, Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy, Machine Learning, 51 (2) (2003), pp. 181–207
[5] A Basic Introduction to Neural Networks. <http://www.pages.cs.wisc.edu/~bolo/shipyard/neural/local.html> (accessed August 2012).
[6] MathWorks, Support Vector Machines (SVM). <http://www.mathworks.com.au/help/toolbox/bioinfo/ug/bs3tbev-1.html> (accessed August 2012).
[7] R. Maclin, J.W. Shavlik, Combining the predictions of multiple classifiers: using competitive learning to initialize neural networks, in: International Joint Conference on Artificial Intelligence, 1995, pp. 524–531.
[8] T. Yamaguchi, K.J. Mackin, E. Nunohiro, J.G. Park, K. Hara, K. Matsushita, M. Ohshiro, K. Yamasaki, Artificial neural network ensemble-based land-cover classifiers using MODIS data, Artificial Life and Robotics, 13 (2) (2009), pp. 570–574.
[9] H. Chen, X. Yao, Regularized negative correlation learning for neural network ensembles, IEEE Transactions on Neural Networks, 20 (12) (2009), pp. 1962–1979.
[10] H. Chen, X. Yao, Multiobjective neural network ensembles based on regularized negative correlation learning, IEEE Transactions on Knowledge and Data Engineering, 22 (12) (2010), pp. 1738–1751.
[11] T.K. Ho, The random subspace method for constructing decision forests, IEEE Transaction on Pattern Analysis and Machine Intelligence, 20 (8) (1998), pp. 832–844.
[12] A. Bertoni, R. Folgieri, G. Valentini, Bio-molecular cancer prediction with random subspace ensembles of support vector machines, Neurocomputing, 63 (2005), pp. 535– 539.
[13] L.I. Kuncheva, J.J. Rodriguez, C.O. Plumpton, D.E. Linden, S.J. Johnston, Random subspace ensembles for FMRI classification, IEEE Transaction on Medical Imaging, 29 (2) (2010), pp. 531–542.
[14] G. Martínez-Muñoz, A. Sánchez-Martínez, D. Hernández-Lobato, A. Suarez, Class-switching neural network ensembles, Neurocomputing, 7 (2008), pp. 2521–2528.
[15] T.G. Dietterich, G. Bakiri, Solving multiclass learning problems via error-correcting output codes, Journal of Artificial Intelligence Research, 2 (1995), pp. 263–286
[16] L. Rokach, O. Maimon, I. Lavi, Space decomposition in data mining: a clustering approach, in: International Symposium on Methodologies for Intelligent Systems, 2003, pp. 24–31.
[17] J. Xiuping, J.A. Richards, Cluster-space classification: a fast k-nearest neighbour classification for remote sensing hyperspectral data, in: IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003, pp. 407–410.
[18] L.I. Kuncheva, Cluster-and-selection method for classifier combination, in: International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies (KES), 2000, pp. 185–188.
[19] B. Tang, M.I. Heywood, M. Shepherd, Input partitioning to mixture of experts, in: International Joint Conference on Neural Networks, 2002, pp. 227–232.
[20] G. Nasierding, G. Tsoumakas, A.Z. Kouzani, Clustering based multi-label classification for image annotation and retrieval, in: IEEE International Conference on Systems, Man and Cybernetics, 2009, pp. 4514–4519.
[21] S. Eschrich, L.O. Hall, Soft partitions lead to better learned ensemble, in: Annual meeting of the North American fuzzy information processing society (NAFIPS), 2002, pp. 406–411.
[22] M.J. Jordan, R.A. Jacobs, Hierarchical mixtures of experts and the EM algorithm, Neural Computation, 6 (2) (1994), pp. 181–214.
[23] A. Rahman and B. Verma, Cluster Based Ensemble of Classifiers, Wiley Expert Systems, DOI: DOI: 10.1111/j.1468-0394.2012.00637.x, 2012.
[24] B. Verma and A. Rahman, Cluster Oriented Ensemble Classifier: Impact of Multi-cluster Characterisation on Ensemble Classifier Learning, IEEE Transaction on Knowledge and Data Engineering, vol. 24, no. 4, pp. 605–618, 2012.
[25] A. Rahman and B. Verma, A Novel Layered Clustering based Approach for Generating Ensemble of Classifiers, IEEE Transaction on Neural Networks, vol 22, no 5, pp 781– 792, 2011.
[26] A. Rahman and B. Verma, A Novel Ensemble Classifier Approach using Weak Classifier Learning on Overlapping Clusters, Proc. IEEE International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain, 2010.
[27] A. Rahman and B. Verma, Influence of Unstable Patterns in Layered Cluster Oriented Ensemble Classifier, Proc. IEEE International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia, 2012.
[28] A. Rahman and B. Verma, Cluster Based Ensemble Classifier Generation by Joint Optimization of Accuracy and Diversity, International Journal of Computational Intelligence and Applications, vol. 12, no. 4, DOI: 10.1142/S1469026813400038, 2013.
[29] A. Rahman and B. Verma, Ensemble Classifier Generation using Non–uniform Layered Clustering and Genetic Algorithm, Elsevier Knowledge Based Systems, vol. 43 (May 2013), pp. 30–42, 2013.
[30] A. Rahman and B. Verma, Cluster Oriented Ensemble Classifiers using Multi–Objective Evolutionary Algorithm, Proc. IEEE International Joint Conference on Neural Networks (IJCNN), pp. 829–834, Dallas, Texas, 2013.
[31] A. Rahman, B. Verma, and X. Yao, Non–uniform Layered Clustering for Ensemble Classifier Generation and Optimality, 19th International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science, vol. 6443, pp. 551–558, 2010.
[32] L. Breiman, Bagging predictors, Machine Learning, 24 (2) (1996), pp. 123–140
[33] L. Breiman, Random forests, Machine Learning, 45 (1) (2001), pp. 5–32
[34] L. Breiman, Pasting small votes for classification in large databases and on-line, Machine Learning, 36 (1999), pp. 85–103
[35] R.E. Schapire, The strength of weak learnability, Machine Learning, 5 (2) (1990), pp. 197–227
[36] Y. Freund, R.E. Schapire, Decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Sciences, 55 (1) (1997), pp. 119–139
[37] A. Rahman, D. Smith, and G. Timms, A Novel Machine Learning Approach towards Quality Assessment of Sensor Data, IEEE Sensors Journal, DOI: 10.1109/JSEN.2013.2291855.
[38] A. Rahman, D. Smith, and G. Timms Multiple Classifier System for Automated Quality Assessment of Marine Sensor Data, Proceedings IEEE Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 362–367, Melbourne, 2013.
[39] C. D’ Este, A. Rahman, and A. Turnbull, Predicting Shellfish Farm Closures with Class Balancing Methods, AAI 2012: Advances in Artificial Intelligence, Lecture Notes in Computer Science, pp. 39–48, 2012.
[40] A. Rahman, C. D`Este, and J. McCulloch, Ensemble Feature Ranking for Shellfish Farm Closure Cause Identification, Workshop on Machine Learning for Sensory Data Analysis hosted with Australian AI conference, DOI: 10.1145/2542652.2542655, 2013.
[41] A. Rahman and B. Verma, Effect of Ensemble Classifier Composition on Offline Cursive Character Recognition, Elsevier Information Processing & Management, vol. 49, issue 4, July 2013, pp. 852–864, 10.1016/j.ipm.2012.12.010, 2013.
[42] A. Rahman and B. Verma, Ensemble Classifier Composition: Impact on Feature Based Offline Cursive Character Recognition, Proc. IEEE International Joint Conference on Neural Networks (IJCNN), San Jose, USA, 2011.
[43] A. Rahman, Benthic Habitat Mapping from Seabed Images using Ensemble of Color, Texture, and Edge Features, International Journal of Computational Intelligence Systems, vol. 6, no. 6, pp. 1072-1081, 2013.
[44] A. Rahman, Claire D’ Este, and G. Timms, Dealing with Missing Sensor Values in Predicting Shellfish Farm Closure, Proceedings IEEE Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 351–356, Melbourne, 2013.
[45] Q. Zhang, A. Rahman, and C. D`Este, Impute vs. Ignore: Missing Values for Prediction, Proc. IEEE International Joint Conference on Neural Networks (IJCNN), pp. 2193– 2200, Dallas, Texas, 2013.
[46] A. Rahman and M. Murshed, “Feature weighting methods for abstract features applicable to motion based video indexing,” IEEE International Conference on Information Technology: Coding and Computing (ITCC), vol. 1, pp. 676–680, USA, 2004.
[47] A. Rahman and MS Shahriar, Algae Growth Prediction through Identification of Influential Environmental Variables: A Machine Learning Approach, International Journal of Computational Intelligence and Applications, vol. 12, no. 2, DOI: 10.1142/S1469026813500089, 2013.
Keywords
Ensemble classifier, Multiple classifier systems, Mixture of experts