A new classifier ensemble methodology based on subspace learning

References

• Alpaydin , E and Kaynak , C . 1998 . Cascading Classifiers . Kybernetika , 34 : 369 – 374 .
   Web of Science ®Google Scholar
• Bandyopadhyay , S and Muthy , CA . 1995 . Pattern Classification Using Genetic Algorithms . Pattern Recognition Letters , 16 : 801 – 808 .
   Web of Science ®Google Scholar
• Bala , J , De Jong , K , Huang , J , Vafaie , H and Wechsler , H . 1997 . Using Learning to Facilitate the Evolution of Features for Recognizing Visual Concepts . Evolutionary Computation , 4 : 297 – 311 .
   Web of Science ®Google Scholar
• Blake , CL and Merz , CJ . (1998), ‘UCI Repository of machine learning databases’, http://www.ics.uci.edu/~mlearn/ MLRepository.html
   Google Scholar
• Breiman , L . 1996 . Bagging Predictors . Machine Learning , 24 : 123 – 140 .
   Web of Science ®Google Scholar
• Breiman , L . 1998 . Arcing Classifiers . The Annals of Statistics , 26 : 801 – 849 .
   Web of Science ®Google Scholar
• Breiman , L . 2001 . Random Forests . Machine Learning , 45 : 5 – 32 .
   Web of Science ®Google Scholar
• Cortes , C and Vapnik , V . 1995 . Support-Vector Networks . Machine Learning , 20 : 273 – 297 .
   Web of Science ®Google Scholar
• De Jong , KA , Spears , WM and Gordon , DF . 1993 . Using Genetic Algorithms for Concept Learning . Machine Learning , 13 : 161 – 188 .
   Web of Science ®Google Scholar
• Dietrich , TG . 1997 . Machine-Learning Research: Four Current Directions . AI Magazine , 18 : 97 – 135 .
   Web of Science ®Google Scholar
• Dietterich , TG . 2002 . “ Ensemble Learning ” . In The Handbook of Brain Theory and Neural Networks , 2nd , Edited by: Arbib , MA . Cambridge , MA : MIT Press .
   Google Scholar
• Dimililer , N . Varoglu, E. and Altıncay, H. (2007), ‘Vote-Based Classifier Selection for Biomedical NER Using Genetic Algorithms’, in Proceedings of the 3rd Iberian Conference on Pattern Recognition and Image Analysis, Gerona, Spain, pp. 202–209
   Google Scholar
• Duda , RO , Hart , PE and Stork , DG . 2001 . Pattern Classification , 2nd , New York : John Wiley & Sons .
   Google Scholar
• Efron , B and Tibshirani , R . 1993 . An Introduction to the Bootstrap , New York : Chapman & Hall .
   Google Scholar
• Falkenauer , E . 1998 . Genetic Algorithms and Grouping Problems , New York : John Wiley & Sons .
   Google Scholar
• Freitas , AA . (2002), ‘A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery’, Advances in Evolutionary Computation, pp. 819–845, Springer-Verlag, New York
   Google Scholar
• Freund , Y and Schapire , RE . (1995), ‘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
   Google Scholar
• Friedman , JH . 1989 . Regularized Discriminant Analysis . Journal of the American Statistical Association , 84 : 165 – 175 .
   Web of Science ®Google Scholar
• Guerra-Salcedo , C and Whitley , D . 1999 . “ Feature Selection Mechanisms for Ensemble Creation: A Genetic Search Perspective ” . In Data Mining with Evolutionary Algorithms: Research Directions–Papers from the AAAI Workshop, Technical Report WS-99-06 , Edited by: Freitas , AA . 13 – 17 . Palo Alto, CA : AAAI Press .
   Google Scholar
• Gunter , S . and Bunke, H. (2002), ‘Creation of Classifier Ensembles for Handwritten Word Recognition Using Feature Selection Algorithms’, in 8th International Workshop on Frontiers in Handwriting, pp. 183–188
   Google Scholar
• Hansen , LK and Salamon , P . 1990 . Neural Network Ensembles . IEEE Transaction on Pattern Analysis and Machine Intelligence , 12 : 993 – 1001 .
   Web of Science ®Google Scholar
• Jain , AK and Zongker , D . 1997 . Feature Selection: Evaluation, Application, and Small Sample Performance . IEEE Transaction on Pattern Analysis and Machine Intelligence , 19 : 153 – 158 .
   Web of Science ®Google Scholar
• Jain , AK , Duin , RPW and Mao , J . 2000 . Satanical Pattern Recognition: A Review . IEEE Transaction on Pattern Analysis and Machine Intelligence , 22 : 4 – 37 .
   Web of Science ®Google Scholar
• Kohonen , T . (1990), ‘Improved Versions of Learning Vector Quantization’, in Proceedings of International Joint Conference on Neural Networks, San Diego, CA. I545–I550
   Google Scholar
• Krogh , A and Vedelsdy , J . 1995 . “ Neural Network Ensembles Cross Validation, and Active Learning ” . In Advances in Neural Information Processing Systems 7 , Edited by: Tesauro , G , Touretzky , D and Leen , T . 231 – 238 . Cambridge , MA : MIT Press .
   Google Scholar
• Kuncheva , LI . 1993 . Change-Glasses Approach in Pattern Recognition . Pattern Recognition Letters , 14 : 619 – 623 .
   Web of Science ®Google Scholar
• Kuncheva , LI . 2005 . Combining Pattern Classifiers, Methods and Algorithms , New York : Wiley .
   Google Scholar
• Lam , L . 2000 . “ Classifier Combinations: Implementations and Theoretical Issues ” . In Multiple Classifier Systems 1857 of Lecture Notes in Computer Science , Edited by: Kittler , J , F and Roli . 78 – 86 . Cagliari : Springer .
   Google Scholar
• Martin-Bautista , MJ . and Vila, M.A. (1999), ‘A Survey of Genetic Feature Selection in Mining Issues’, in Proceeding Congress on Evolutionary Computation (CEC-99), Washington, D.C., pp. 1314–1321
   Google Scholar
• Martinez , AM and Kak , AC . 2001 . PCA versus LDA . IEEE Transactions on Pattern Analysis and Machine Intelligence , 23 : 228 – 233 .
   Web of Science ®Google Scholar
• Michalewicz , Z . 1996 . Genetic Algorithms + Data Structures = Evolution Programs , 3rd , Berlin : Springer-Verlag .
   Google Scholar
• Muhlenbein , H and Schlierkamp-Voosen , D . 1993 . Predictive Models for the Breeder Genetic Algorithm: I . Continuous Parameter Optimization. Evolutionary Computation , 1 : 25 – 49 .
   Web of Science ®Google Scholar
• Parvin , H , Minaei-Bidgoli , B , Ghatei , S and Alinejad-Rokny , H . 2011 . An Innovative Combination of Particle Swarm Optimization . Learning Automaton and Great Deluge Algorithms for Dynamic Environments. International Journal of the Physical Sciences , 6 : 5121 – 5127 .
   Google Scholar
• Parvin , H . Alizadeh, H., and Minaei-Bidgoli, B. (2008b). ‘A New Approach to Improve the Vote-Based Classifier Selection’, in Proceedings of International Conference on Networked Computing and Advanced Information Management, NCM08, Korea, 2. Washington, D.C: IEEE Computer Society, pp. 91–95
   Google Scholar
• Parvin , H , Alizadeh , H and Minaei-Bidgoli , B . 2008c . ‘Validation Based Modified K-Nearest Neighbor’ . in IAENG Transactions on Engineering Technologies, II–Special Edition of the World Congress on Engineering and Computer Science , 1127 : 153 – 161 .
   Google Scholar
• Parvin , H . Alizadeh, H., Minaei-Bidgoli, B. and Analoui, M. (2008d). ‘A Scalable Method for Improving the Performance of Classifiers in Multiclass Applications by Pairwise Classifiers and GA’, in Proceedings of the International Conference on Networked Computing and Advanced Information Management, NCM08, Korea, 2. Washington, D.C.: IEEE Computer Society, pp. 137–142
   Google Scholar
• Parvin , H . Alizadeh, H., Moshki, M., Minaei-Bidgoli, B. and Mozayani, N. (2008e). ‘Divide & Conquer Classification and Optimization by Genetic Algorithm’, in Proceedings of the International Conference on Convergence and Hybrid Information Technology, ICCIT08, Busan, Korea, 2. Washington, D.C.: IEEE Computer Society, pp. 858–863
   Google Scholar
• Puuronen , S . Tsymbal, A., and Skrypnyk, I. (2001), ‘Correlation-based and Contextual Meritbased Ensemble Feature Selection’, in Proceedings of 4th International Conference on Advances in Intelligent Data Analysis, IDA’01. 2189, of Lecture Notes in Computer Science, Cascais, Portugal, pp. 135–144
   Google Scholar
• Qiang , F , Shang-xu , H and Sheng-ying , Z . 2005 . Clustering-based Selective Neural Network Ensemble . Journal of Zhejiang University SCIENCE , 5 : 387 – 392 .
   Google Scholar
• Rastrigin , LA and Erenstein , RH . 1981 . Method of Collective Recognition , Moscow : Energoizdat .
   Google Scholar
• Roli , F . and Kittler, J. (eds.) (2001), Proceedings of 2nd International Workshop on Multiple Classifier Systems (MCS) 2096, of Lecture Notes in Computer Science LNCS, Cambridge, UK: Springer-Verlag
   Google Scholar
• Roli , F . Giacinto, G., and Vernazza, G. (2001), ‘Methods for Designing Multiple Classifier Systems’, in Proceedings of 2nd International Workshop on Multiple Classifier Systems 2096, of Lecture Notes in Computer Science, Springer-Verlag, Cambridge, UK, pp. 78–87
   Google Scholar
• Roli , F . and Kittler, J. (eds.) (2002), Proceedings of 3rd Int. Workshop on Multiple Classifier Systems (MCS), 2364, of Lecture Notes in Computer Science LNCS, Cagliari: Springer-Verlag
   Google Scholar
• Saberi , A . Vahidi, M., and Minaei-Bidgoli, B. (2007), ‘Learn to Detect Phishing Scams Using Learning and Ensemble Methods’, in IEEE/WIC/ACM International Conference on Intelligent Agent Technology, Workshops (IAT 07), Silicon Valley, USA, pp. 311–314
   Google Scholar
• Sharkey , JC . (ed.) (1999), Combining Artificial Neural Nets. Ensemble and Modular Multi-Net Systems, London: Springer-Verlag
   Google Scholar
• Skalak , DB . (1994), ‘Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms’, in Proceedings of 11th International Conference on Machine Learning, New Brunswick, NJ, Morgan Kaufmann, Los Altos, CA, pp. 293–301
   Google Scholar
• Vafaie , H . and De Jong, K. (1993), ‘Robust Feature Selection Algorithms’, in Proceeding of IEEE International Conference on Tools with AI, Boston, Mass., USA, pp. 356–363
   Google Scholar
• Woods , K , Kegelmeyer , WP and Bowyer , K . 1997 . Combination of Multiple Classifiers Using Local Accuracy Estimates . IEEE Transactions on Pattern Analysis and Machine Intelligence , 19 : 405 – 410 .
   Web of Science ®Google Scholar