L. Breiman,.: Bagging predictors. Machine Learning, 24(2): 123-140, 1996.
 E. Ghanbari, H. Beigy: Incremental RotBoost algorithm: An application for spam filtering. Intell. Data Anal. 19(2): 449-468, 2015.
 R.O Duda., P.E. Hart., and D.G. Stork.: Pattern Classification. 2nd ed. John Wiley & Sons, NY,.B. Smith, “An approach to graphs of linear forms (Unpublished work style),” unpublished. 2001.
 Q. Fu, S.X. Hu and S.Y. Zhao,.: A PSO-based approach for neural network ensemble. Journal of Zhejiang University (Engineering Science), 38(12): 1596-1600, 2004 (in Chinese).
 L. Breiman, Random forests, Mach. Learn. 45: 5–32, 2001.
 L. Breiman, Arcing classifiers, Annal. Stat. 26: 801–824, 1998.
 Y. Freund, and R.E. Schapire, “Experiments with a new boosting algorithm”, International Conference on Machine Learning, pp. 148–156, 1996.
 Y. Freund and R.E. Schapire ,”A decision-theoretic generalization of on-line learning and an application to boosting”, J. Comput. Sys. Sci. 55: 119–139, 1997.
 L.I. Kuncheva: Combining Pattern Classifiers, Methods and Algorithms, New York: Wiley, 2005.
 Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
 A. Lazarevic, and Z. Obradovic,: Effective pruning of neural network classifier ensembles. Proc. International Joint Conference on Neural Networks, 2: 796-801, 2001.
 Y. Liu, X.Yao: Evolutionary ensembles with negative correlation learning. IEEE Trans. Evolutionary Computation, 4(4): 380-387, 2000.
 P. Melville and R. Mooney: Constructing Diverse Classifier Ensembles Using Artificial Training Examples. Proc. of the IJCAI-2003, p.505-510, 2003.
 B. Minaei-Bidgoli, G. Kortemeyer, and W.F Punch.: Optimizing Classification Ensembles via a Genetic Algorithm for a Web-based Educational System. Lecture Notes in Computer Science 3138: 397-406, 2004.
 B. Minaei-Bidgoli, H. Parvin, H. Alinejad-Rokny, H. Alizadeh, W.F Punch.: Effects of resampling method and adaptation on clustering ensemble efficacy. Artif. Intell. Rev. 41(1): 27-48 2014.
 H.D. Navone, P.F. Verdes, P.M. Granitto and H.A. Ceccatto: Selecting Diverse Members of Neural Network Ensembles. Proc. 16th Brazilian Symposium on Neural Networks, p.255-260, 2000.
 D. Opitz, and J. Shavlik,: Actively searching for an effective neural network ensemble. Connection Science, 8(3-4): 337-353, 1996.
 H. Parvin, H. Helmi, B. Minaei-Bidgoli, H. Alinejad-Rokny, and H. Shirgahi: Linkage Learning Based on Differences in Local Optimums of Building Blocks with One Optima. International Journal of the Physical Sciences 6(14): 3419–3425, 2011.
 H. Parvin, B. Minaei-Bidgoli, S. Ghatei, and H. Alinejad-Rokny,: An Innovative Combination of Particle Swarm Optimization, Learning Automaton and Great Deluge Algorithms for Dynamic Environments. International Journal of the Physical Sciences 6(22): 5121 – 5127, 2011.
 A. Krogh, and J. Vedelsdy,: Neural Network Ensembles Cross Validation, and Active Learning. Advances in Neural Information Processing Systems, 7: 231-238, 1995.
 H.R. Qodmanan, M. Nasiri, B. Minaei-Bidgoli: Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence, Expert Systems with Applications, 38(1): 288-298, 2011.
 F. Roli and J. Kittler, Proc. of 2nd International Workshop on Multiple Classifier Systems, Vol. 2096 of Lecture Notes in Computer Science LNCS Springer- Verlag, Cambridge, UK, 2001.
 F. Roli and J. Kittler, Proc. of 3rd Int. Workshop on Multiple Classifier Systems, Vol. 2364 of Lecture Notes in Computer Science LNCS Springer Verlag, Cagliari, Italy, 2002.
 B.E. Rosen,: Ensemble learning using decorrelated neural network. Connection Science, 8(3-4): 373-384, 1996.
 R.E. Schapire,: The strength of weak learn ability. Machine Learning, 5(2):1971-227, 1990.
 Z.H. Zhou, J.X. Wu, Y. Jiang, and S.F. Chen: Genetic algorithm based selective neural network ensemble. Proc. 17th International Joint Conference on Artificial Intelligence, 2: 797-802, 2001.
 H. Parvin, B. Minaei-Bidgoli: A clustering ensemble framework based on selection of fuzzy weighted clusters in a locally adaptive clustering algorithm. Pattern Anal. Appl. 18(1): 87-112, 2015.
 M.H. Fouladgar, B. Minaei-Bidgoli, and H. Parvin: On Possibility of Conditional Invariant Detection. 6881(2): 214-224, 2011.
 H. Parvin, B. Minaei-Bidgoli, and H. Alizadeh: Detection of Cancer Patients Using an Innovative Method for Learning at Imbalanced Datasets. LNCS 6954: 376-381, 2011.
 M. Daryabari, B. Minaei-Bidgoli, and H. Parvin: Localizing Program Logical Errors Using Extraction of Knowledge from Invariants. LNCS 6630: 124-135, 2011.
 H. Parvin, B. Minaei-Bidgoli and S. Parvin: A Metric to Evaluate a Cluster by Eliminating Effect of Complement Cluster. LNCS 7006: 246-254, 2011.
 H. Parvin, B. Minaei-Bidgoli: Linkage Learning Based on Local Optima. LNCS 6922(1): 163-172, 2011.
 H. Parvin, B. Minaei-Bidgoli, and H. Alizadeh and A. Beigi: A Novel Classifier Ensemble Method Based on Class Weightening in Huge Dataset. LNCS 6676 (2): 144-150, 2011.
 H. Parvin, B. Minaei-Bidgoli and H. Ghaffarian: An Innovative Feature Selection Using Fuzzy Entropy. LNCS 6677 (3): 576-585, 2011.
 H. Parvin, B. Minaei, H. Karshenas, and A. Beigi: A New N-gram Feature Extraction-Selection Method for Malicious Code. LNCS 6594(2): 98-107, 2011.