[Bache, K. and Lichman, M. (2013). UCI machine learning repository, http://archive.ics.uci.edu/ml.]Search in Google Scholar
[Berger, J.O. and Berger, J.O. (1985). Statistical Decision Theory and Bayesian Analysis, Springer-Verlag, New York, NY.10.1007/978-1-4757-4286-2]Search in Google Scholar
[Bishop, C. (1995). Neural Networks for Pattern Recognition, Clarendon Press/Oxford University Press, Oxford/New York, NY.]Search in Google Scholar
[Blum, A. (1998). On-line algorithms in machine learning, in A. Fiat and G.J. Woeginger (Eds.), Developments from a June 1996 Seminar on Online Algorithms: The State of the Art, Springer-Verlag, London, pp. 306–325.10.1007/BFb0029575]Search in Google Scholar
[Breiman, L. (1996). Bagging predictors, Machine Learning24(2): 123–140.10.1007/BF00058655]Search in Google Scholar
[Breiman, L., Friedman, J., Olshen, R. and Stone, C. (1984). Classification and Regression Trees, Wadsworth and Brooks, Monterey, CA.]Search in Google Scholar
[Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification, IEEE Transactions on Information Theory13(1): 21–27, DOI:10.1109/TIT.1967.1053964.10.1109/TIT.1967.1053964]Search in Google Scholar
[Dai, Q. (2013). A competitive ensemble pruning approach based on cross-validation technique, Knowledge-Based Systems37(9): 394–414, DOI: 10.1016/j.knosys.2012.08.024.10.1016/j.knosys.2012.08.024]Search in Google Scholar
[Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets, The Journal of Machine Learning Research7: 1–30.]Search in Google Scholar
[Devroye, L., Györfi, L. and Lugosi, G. (1996). A Probabilistic Theory of Pattern Recognition, Springer, New York, NY.10.1007/978-1-4612-0711-5]Search in Google Scholar
[Didaci, L., Giacinto, G., Roli, F. and Marcialis, G.L. (2005). A study on the performances of dynamic classifier selection based on local accuracy estimation, Pattern Recognition38(11): 2188–2191.10.1016/j.patcog.2005.02.010]Search in Google Scholar
[Dietterich, T.G. (2000). Ensemble methods in machine learning, Proceedings of the 1st International Workshop on Multiple Classifier Systems, MCS’00, Cagliari, Italy, pp. 1–15.]Search in Google Scholar
[Dunn, O.J. (1961). Multiple comparisons among means, Journal of the American Statistical Association56(293): 52–64.10.1080/01621459.1961.10482090]Search in Google Scholar
[Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G. and Barman, S. (2012). An ensemble classification-based approach applied to retinal blood vessel segmentation, IEEE Transactions on Biomedical Engineering59(9): 2538–2548.10.1109/TBME.2012.2205687]Search in Google Scholar
[Freund, Y. and Shapire, R. (1996). Experiments with a new boosting algorithm, Machine Learning: Proceedings of the 13th International Conference, Bari, Italy, pp. 148–156.]Search in Google Scholar
[Friedman, M. (1940). A comparison of alternative tests of significance for the problem of m rankings, The Annals of Mathematical Statistics11(1): 86–92, DOI: 10.2307/2235971.]Search in Google Scholar
[Gama, J. (2010). Knowledge Discovery from Data Streams, 1st Edn., Chapman & Hall/CRC, London.]Search in Google Scholar
[Giacinto, G. and Roli, F. (2001). Dynamic classifier selection based on multiple classifier behaviour, Pattern Recognition34(9): 1879–1881.10.1016/S0031-3203(00)00150-3]Search in Google Scholar
[Holm, S. (1979). A simple sequentially rejective multiple test procedure, Scandinavian Journal of Statistics6(2): 65–70.]Search in Google Scholar
[Hsieh, N.-C. and Hung, L.-P. (2010). A data driven ensemble classifier for credit scoring analysis, Expert systems with Applications37(1): 534–545.10.1016/j.eswa.2009.05.059]Search in Google Scholar
[Huenupán, F., Yoma, N.B., Molina, C. and Garretón, C. (2008). Confidence based multiple classifier fusion in speaker verification, Pattern Recognition Letters29(7): 957–966.10.1016/j.patrec.2008.01.015]Search in Google Scholar
[Jurek, A., Bi, Y., Wu, S. and Nugent, C. (2013). A survey of commonly used ensemble-based classification techniques, The Knowledge Engineering Review29(5): 551–581, DOI: 10.1017/s0269888913000155.10.1017/S0269888913000155]Search in Google Scholar
[Kittler, J. (1998). Combining classifiers: A theoretical framework, Pattern Analysis and Applications1(1): 18–27.10.1007/BF01238023]Search in Google Scholar
[Ko, A.H., Sabourin, R. and Britto, Jr., A.S. (2008). From dynamic classifier selection to dynamic ensemble selection, Pattern Recognition41(5): 1718–1731.10.1016/j.patcog.2007.10.015]Search in Google Scholar
[Kuncheva, L.I. (2004). Combining Pattern Classifiers: Methods and Algorithms, 1st Edn., Wiley-Interscience, New York, NY.]Search in Google Scholar
[Kuncheva, L.I. and Rodríguez, J.J. (2014). A weighted voting framework for classifiers ensembles, Knowledge-Based Systems38(2): 259–275.10.1007/s10115-012-0586-6]Search in Google Scholar
[Kurzynski, M. (1987). Diagnosis of acute abdominal pain using three-stage classifier, Computers in Biology and Medicine17(1): 19–27.10.1016/0010-4825(87)90030-8]Search in Google Scholar
[Kurzynski, M., Krysmann, M., Trajdos, P. and Wolczowski, A. (2014). Two-stage multiclassifier system with correction of competence of base classifiers applied to the control of bioprosthetic hand, IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2014, Limassol, Cyprus.10.1109/ICTAI.2014.98]Search in Google Scholar
[Kurzynski, M. and Wolczowski, A. (2012). Control system of bioprosthetic hand based on advanced analysis of biosignals and feedback from the prosthesis sensors, Proceedings of the 3rd International Conference on Information Technologies in Biomedicine, ITIB 12, Kamień Śląski, Poland, pp. 199–208.]Search in Google Scholar
[Mamoni, D. (2013). On cardinality of fuzzy sets, International Journal of Intelligent Systems and Applications5(6): 47–52.10.5815/ijisa.2013.06.06]Search in Google Scholar
[Plumpton, C.O. (2014). Semi-supervised ensemble update strategies for on-line classification of FMRI data, Pattern Recognition Letters37: 172–177.10.1016/j.patrec.2013.03.029]Search in Google Scholar
[Plumpton, C.O., Kuncheva, L.I., Oosterhof, N.N. and Johnston, S.J. (2012). Naive random subspace ensemble with linear classifiers for real-time classification of FMRI data, Pattern Recognition45(6): 2101–2108.10.1016/j.patcog.2011.04.023]Search in Google Scholar
[R Core Team (2012). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, http://www.R-project.org/.]Search in Google Scholar
[Rokach, L. (2010). Ensemble-based classifiers, Artificial Intelligence Review33(1–2): 1–39.10.1007/s10462-009-9124-7]Search in Google Scholar
[Rokach, L. and Maimon, O. (2005). Clustering methods, Data Mining and Knowledge Discovery Handbook, Springer Science + Business Media, New York, NY, pp. 321–352.]Search in Google Scholar
[Rousseeuw, P. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics20(1): 53–65.10.1016/0377-0427(87)90125-7]Search in Google Scholar
[Scholkopf, B. and Smola, A.J. (2001). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, Cambridge, MA.]Search in Google Scholar
[Tahir, M.A., Kittler, J. and Bouridane, A. (2012). Multilabel classification using heterogeneous ensemble of multi-label classifiers, Pattern Recognition Letters33(5): 513–523.10.1016/j.patrec.2011.10.019]Search in Google Scholar
[Tsoumakas, G., Katakis, I. and Vlahavas, I. (2010). Random k-labelsets for multi-label classification, IEEE Transactions on Knowledge and Data Engineering99(1): 1079–1089.10.1109/TKDE.2010.164]Search in Google Scholar
[Valdovinos, R. and Sánchez, J. (2009). Combining multiple classifiers with dynamic weighted voting, in E. Corchado et al. (Eds.), Hybrid Artificial Intelligence Systems, Lecture Notes in Computer Science, Vol. 5572, Springer, Berlin/Heidelberg, pp. 510–516.10.1007/978-3-642-02319-4_61]Search in Google Scholar
[Ward, J. (1963). Hierarchical grouping to optimize an objective function, Journal of the American Statistical Association58(301): 236–244.10.1080/01621459.1963.10500845]Search in Google Scholar
[Wilcoxon, F. (1945). Individual comparisons by ranking methods, Biometrics Bulletin1(6): 80–83.10.2307/3001968]Search in Google Scholar
[Woloszynski, T. (2013). Classifier competence based on probabilistic modeling (ccprmod.m) at Matlab central file exchange, http://www.mathworks.com/matlabcentral/fileexchange/28391-a-probabilistic-model-of-classifier-competence.]Search in Google Scholar
[Woloszynski, T. and Kurzynski, M. (2011). A probabilistic model of classifier competence for dynamic ensemble selection, Pattern Recognition44(10–11): 2656–2668.10.1016/j.patcog.2011.03.020]Search in Google Scholar
[Woloszynski, T., Kurzynski, M., Podsiadlo, P. and Stachowiak, G.W. (2012). A measure of competence based on random classification for dynamic ensemble selection, Information Fusion13(3): 207–213.10.1016/j.inffus.2011.03.007]Search in Google Scholar
[Wolpert, D.H. (1992). Stacked generalization, Neural Networks5(2): 214–259.10.1016/S0893-6080(05)80023-1]Search in Google Scholar
[Wozniak, M., Graña, M. and Corchado, E. (2014). A survey of multiple classifier systems as hybrid systems, Information Fusion16(1): 3–17.10.1016/j.inffus.2013.04.006]Search in Google Scholar