R. Alejo, J. Sotoca, and G. Casañ, An Empirical Study for the Multi-class Imbalance Problem with Neural Networks, Progress in Pattern Recognition, pp.479-486, 2008.
DOI : 10.1007/3-540-49430-8_15

H. Alhammady and K. Ramamohanarao, The Application of Emerging Patterns for Improving the Quality of Rare-Class Classification, Advances in Knowledge Discovery and Data Mining, pp.207-211, 2004.
DOI : 10.1007/978-3-540-24775-3_27

E. L. Allwein, R. E. Schapire, and Y. Singer, Reducing multiclass to binary: a unifying approach for margin classifiers, Journal of Machine Learning Research, vol.1, pp.113-141, 2001.

S. Amari and H. Nagaoka, Methods of Information Geometry, 2000.

F. Angiulli, Fast condensed nearest neighbor rule, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.25-32, 2005.
DOI : 10.1145/1102351.1102355
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.8450

A. Asuncion and D. J. Newman, UCI machine learning repository, 2007.

K. Bache and M. Lichman, UCI machine learning repository, 2013.

A. Banerjee, S. Merugu, I. Dhillon, and J. Ghosh, Clustering with Bregman Divergences, J. of Mach. Learn. Res, vol.6, pp.1705-1749, 2005.
DOI : 10.1137/1.9781611972740.22

R. Barandela, J. S. Sanchez, V. Garca, and E. Rangel, Strategies for learning in class imbalance problems, Pattern Recognition, vol.36, issue.3, pp.849-851, 2003.
DOI : 10.1016/S0031-3203(02)00257-1

H. B. Barlow, Unsupervised Learning, Neural Computation, vol.4, issue.3, pp.295-311, 1989.
DOI : 10.1007/BF00288907

A. Barron, A. Cohen, W. Dahmen, and R. Devore, Approximation and learning by greedy algorithms, The Annals of Statistics, vol.36, issue.1, pp.64-94, 2008.
DOI : 10.1214/009053607000000631
URL : http://arxiv.org/abs/0803.1718

P. Bartlett, M. Jordan, and J. D. Mcauliffe, Convexity, Classification, and Risk Bounds, Journal of the American Statistical Association, vol.101, issue.473, pp.138-156, 2006.
DOI : 10.1198/016214505000000907
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.3497

G. E. Batista, R. C. Prati, and M. C. Monard, A study of the behavior of several methods for balancing machine learning training data, ACM SIGKDD Explorations Newsletter, vol.6, issue.1, pp.20-29, 2004.
DOI : 10.1145/1007730.1007735

C. M. Bishop and N. M. Nasrabadi, Pattern recognition and machine learning, 2006.

A. Blum, Random Projection, Margins, Kernels, and Feature-Selection, Subspace, Latent Structure and Feature Selection, pp.52-68, 2006.
DOI : 10.1109/18.705570
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.2149

L. Bottou, Large-scale machine learning with stochastic gradient descent, Proc. of COMP- STAT, pp.177-186, 2010.
DOI : 10.1007/978-3-7908-2604-3_16
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.419.462

J. Brank, M. Grobelnik, N. Milic-frayling, and D. Mladenic, Training text classifiers with svm on very few positive examples, 2003.

P. Bühlmann and T. Hothorn, Boosting Algorithms: Regularization, Prediction and Model Fitting, Statistical Science, vol.22, issue.4, pp.477-505, 2007.
DOI : 10.1214/07-STS242

A. Buja, D. Mease, and A. Wyner, Comment: Boosting Algorithms: Regularization, Prediction and Model Fitting, Statistical Science, vol.22, issue.4, pp.506-512, 2007.
DOI : 10.1214/07-STS242B

P. K. Chan and S. J. Stolfo, Toward scalable learning with non-uniform class and cost distributions: A case study in credit card fraud detection, KDD, pp.164-168, 1998.

K. Chatfield, V. Lempitsky, A. Vedaldi, and A. Zisserman, The devil is in the details: an evaluation of recent feature encoding methods, Procedings of the British Machine Vision Conference 2011, pp.1-12, 2011.
DOI : 10.5244/C.25.76

N. Chawla and L. Hall, Smote: Synthetic minority over-sampling technique, J. of Artificial Intelligence Research, issue.16, pp.321-357, 2002.

N. V. Chawla, A. Lazarevic, L. O. Hall, and K. W. Bowyer, SMOTEBoost: Improving Prediction of the Minority Class in Boosting, Knowledge Discovery in Databases: PKDD 2003, pp.107-119, 2003.
DOI : 10.1007/978-3-540-39804-2_12

K. Chen, B. Lu, and J. Kwok, Efficient classification of multi-label and imbalanced data using min-max modular classifiers, Neural Networks, 2006. IJCNN'06. International Joint Conference on, pp.1770-1775, 2006.

Y. Chen, X. S. Zhou, and T. S. Huang, One-class svm for learning in image retrieval, Image Processing Proceedings. 2001 International Conference on, pp.34-37, 2001.

W. W. Cohen, Fast Effective Rule Induction, ICML, pp.115-123, 1995.
DOI : 10.1016/B978-1-55860-377-6.50023-2
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.107.2612

L. P. Cordella, Reliability Parameters to Improve Combination Strategies in Multi-Expert Systems, Pattern Analysis & Applications, vol.2, issue.3, pp.205-214, 1999.
DOI : 10.1007/s100440050029

T. Cover and P. Hart, Nearest neighbor pattern classification, IEEE Transactions on Information Theory, vol.13, issue.1, pp.21-27, 1967.
DOI : 10.1109/TIT.1967.1053964

L. Cucala, J. Marin, C. Robert, and D. Titterington, A Bayesian Reassessment of Nearest-Neighbor Classification, Journal of the American Statistical Association, vol.104, issue.485, pp.263-273, 2009.
DOI : 10.1198/jasa.2009.0125

R. D. Ambrosio, G. Iannello, and P. Soda, A one-per-class reconstruction rule for class imbalance learning, Pattern Recognition (ICPR), 2012 21st International Conference on, pp.1310-1313, 2012.

R. D. Ambrosio, G. Iannello, and P. Soda, Biomedical classification tasks by softmax reconstruction in ecoc framework, 6th IEEE International Symposium on Computer-Based Medical Systems, p.2013, 2013.

R. D. Ambrosio, G. Iannello, and P. Soda, Softmax regression for ecoc reconstruction, Image Analysis and Processing?ICIAP 2013, pp.682-691, 2013.

R. D. Ambrosio, R. Nock, W. B. Ali, F. Nielsen, and M. Barlaud, Boosting nearest neighbors for the efficient estimation of posteriors, Machine Learning and Knowledge Discovery in Databases, pp.314-329, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00702771

R. D. Ambrosio and P. Soda, Polichotomies on imbalanced domains by one-per-class compensated reconstruction rule, Structural, Syntactic, and Statistical Pattern Recognition, pp.301-309, 2012.

R. D. Ambrosio, P. Soda, and G. Iannello, Automatic facial expression recognition using statistical-like moments, ICIAP 2011, pp.585-594, 2011.

C. De-stefano, C. Sansone, and M. Vento, To reject or not to reject: that is the question-an answer in case of neural classifiers, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol.30, issue.1, pp.84-93, 2000.
DOI : 10.1109/5326.827457

M. D. Del-castillo and J. I. Serrano, A multistrategy approach for digital text categorization from imbalanced documents, ACM SIGKDD Explorations Newsletter, vol.6, issue.1, pp.70-79, 2004.
DOI : 10.1145/1007730.1007740

L. Devroye, L. Györfi, and G. Lugosi, A Probabilistic Theory of Pattern Recognition, 1996.
DOI : 10.1007/978-1-4612-0711-5

T. G. Dietterich, Ensemble Methods in Machine Learning, Multiple classifier systems, pp.1-15, 2000.
DOI : 10.1007/3-540-45014-9_1

T. G. Dietterich and G. Bakiri, Solving multiclass learning problems via error-correcting output codes, Journal of Artificial Intelligence Research, vol.2, pp.263-286, 1995.

P. Domingos, MetaCost, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '99, pp.155-164, 1999.
DOI : 10.1145/312129.312220

G. Dong and J. Li, Efficient mining of emerging patterns, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '99, pp.43-52, 1999.
DOI : 10.1145/312129.312191

C. Drummond and R. C. Holte, C4. 5, class imbalance, and cost sensitivity: why undersampling beats over-sampling, Workshop on Learning from Imbalanced Datasets II, 2003.

A. Estabrooks, T. Jo, and N. Japkowicz, A Multiple Resampling Method for Learning from Imbalanced Data Sets, Computational Intelligence, vol.19, issue.3, pp.18-36, 2004.
DOI : 10.1109/78.668782

W. Fan, S. J. Stolfo, J. Zhang, and P. K. Chan, Adacost: misclassification cost-sensitive boosting, ICML, pp.97-105, 1999.

A. Fernández, M. Jesus, and F. Herrera, Multi-class imbalanced data-sets with linguistic fuzzy rule based classification systems based on pairwise learning. Computational Intelligence for Knowledge-Based Systems Design, pp.89-98, 2010.

A. Fernández, M. J. Jesus, and F. Herrera, On the 2-tuples based genetic tuning performance for fuzzy rule based classification systems in imbalanced data-sets, Information Sciences, vol.180, issue.8, pp.1268-1291, 2010.
DOI : 10.1016/j.ins.2009.12.014

A. Fernández, S. García, M. J. Jesus, and F. Herrera, A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets. Fuzzy Sets and Systems, pp.2378-2398, 2008.

A. Fernández, S. García, and F. Herrera, Addressing the Classification with Imbalanced Data: Open Problems and New Challenges on Class Distribution, Hybrid Artificial Intelligent Systems, pp.1-10, 2011.
DOI : 10.1007/978-3-642-21219-2_1

A. Fernández, V. López, M. Galar, M. José-del-jesus, and F. Herrera, Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches. Knowledge-Based Systems, 2013.

D. Fragoudis, D. Meretakis, and S. Likothanassis, Integrating feature and instance selection for text classification, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '02, pp.501-506, 2002.
DOI : 10.1145/775047.775120

Y. Freund and R. Schapire, Experiments with a new boosting algorithm, Machine Learning- International Workshop Then Conference, pp.148-156, 1996.

J. Friedman, T. Hastie, and R. Tibshirani, Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors), The Annals of Statistics, vol.28, issue.2, pp.337-374, 2000.
DOI : 10.1214/aos/1016218223

J. Fürnkranz, Round robin classification, The Journal of Machine Learning Research, vol.2, pp.721-747, 2002.

S. García, A. Fernández, and F. Herrera, Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems, Applied Soft Computing, vol.9, issue.4, pp.1304-1314, 2009.
DOI : 10.1016/j.asoc.2009.04.004

S. García and F. Herrera, Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy, Evolutionary Computation, vol.344, issue.3, pp.275-306, 2009.
DOI : 10.1016/j.patcog.2006.01.009

N. García-pedrajas and D. Ortiz-boyer, Boosting k-nearest neighbor classifier by means of input space projection, Expert Systems with Applications, vol.36, issue.7, pp.10570-10582, 2009.
DOI : 10.1016/j.eswa.2009.02.065

T. Gneiting and A. Raftery, Strictly Proper Scoring Rules, Prediction, and Estimation, Journal of the American Statistical Association, vol.102, issue.477, pp.359-378, 2007.
DOI : 10.1198/016214506000001437

M. Gonen, A. G. Tanugur, and E. Alpaydm, Multiclass Posterior Probability Support Vector Machines, IEEE Transactions on Neural Networks, vol.19, issue.1, pp.130-139, 2008.
DOI : 10.1109/TNN.2007.903157

G. Griffin, A. Holub, and P. Perona, Caltech-256 object category dataset, 2007.

P. Grünwald and P. Dawid, Game theory, maximum entropy, minimum discrepancy and robust Bayesian decision theory, Annals of Statistics, vol.32, pp.1367-1433, 2004.

X. Guo, Y. Yin, C. Dong, G. Yang, and G. Zhou, On the Class Imbalance Problem, 2008 Fourth International Conference on Natural Computation, pp.192-201, 2008.
DOI : 10.1109/ICNC.2008.871

H. Han, W. Wang, and B. Mao, Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning, Advances in intelligent computing, pp.878-887, 2005.
DOI : 10.1007/11538059_91

S. Han, B. Yuan, and W. Liu, Rare Class Mining: Progress and Prospect, 2009 Chinese Conference on Pattern Recognition, pp.1-5, 2009.
DOI : 10.1109/CCPR.2009.5344137

D. O. Hebb, The organization of behavior: A neuropsychological theory, 2002.

T. K. Ho, J. J. Hull, and S. N. Srihari, Decision combination in multiple classifier systems. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.16, issue.1, pp.66-75, 1994.

K. Huang, H. Yang, I. King, and M. R. Lyu, Learning classifiers from imbalanced data based on biased minimax probability machine, Computer Vision and Pattern Recognition Proceedings of the 2004 IEEE Computer Society Conference on, p.558, 2004.

G. Iannello, On the use of classification reliability for improving performance of the one-per-class decomposition method, Data & Knowledge Engineering, vol.68, issue.12, pp.1398-1410, 2009.
DOI : 10.1016/j.datak.2009.07.003

N. Japkowicz, The class imbalance problem: Significance and strategies, Proceedings of the 2000 International Conference on Artificial Intelligence, pp.111-117, 2000.

N. Japkowicz, Supervised learning with unsupervised output separation, International Conference on Artificial Intelligence and Soft Computing, pp.321-325, 2002.

J. Jelonek and J. Stefanowski, Experiments on solving multiclass learning problems by n 2-classifier, 10th European Conference on Machine Learning Lecture Notes in Artificial Intelligence, pp.172-177, 1998.
DOI : 10.1007/BFb0026687

T. Jo and N. Japkowicz, Class imbalances versus small disjuncts, ACM SIGKDD Explorations Newsletter, vol.6, issue.1, pp.40-49, 2004.
DOI : 10.1145/1007730.1007737

T. Joachims, A support vector method for multivariate performance measures, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.377-384, 2005.
DOI : 10.1145/1102351.1102399

M. V. Joshi, R. C. Agarwal, and V. Kumar, Mining needle in a haystack, ACM SIGMOD Record, vol.30, issue.2, pp.91-102, 2001.
DOI : 10.1145/376284.375673

M. V. Joshi, V. Kumar, and R. C. Agarwal, Evaluating boosting algorithms to classify rare classes: comparison and improvements, Proceedings 2001 IEEE International Conference on Data Mining, pp.257-264, 2001.
DOI : 10.1109/ICDM.2001.989527

L. P. Kaelbling, M. L. Littman, and A. W. Moore, Reinforcement learning: A survey. arXiv preprint cs, 1996.

S. Kakade, S. Shalev-shwartz, and A. Tewari, Applications of strong convexity?strong smoothness duality to learning with matrices, 2009.

T. Kanade, J. Cohn, and Y. Tian, Comprehensive database for facial expression analysis, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580), pp.46-53, 2000.
DOI : 10.1109/AFGR.2000.840611

M. Kearns and Y. Mansour, On the Boosting Ability of Top???Down Decision Tree Learning Algorithms, Journal of Computer and System Sciences, vol.58, issue.1, pp.109-128, 1999.
DOI : 10.1006/jcss.1997.1543

M. J. Kearns and U. V. Vazirani, An Introduction to Computational Learning Theory, 1994.

J. Kittler, M. Hatef, R. P. Duin, and J. Matas, On combining classifiers. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.20, issue.3, pp.226-239, 1998.

S. Kotsiantis, D. Kanellopoulos, and P. Pintelas, Handling imbalanced datasets: A review, GESTS International Transactions on Computer Science and Engineering, vol.30, issue.1, pp.25-36, 2006.

S. Kotsiantis and P. Pintelas, Mixture of expert agents for handling imbalanced data sets, Annals of Mathematics, Computing and Teleinformatics, vol.1, issue.1, pp.46-55, 2003.

M. Kubat and S. Matwin, Addressing the curse of imbalanced training sets: One-sided selection, Machine Learning-International Workshop Then Conference, pp.179-186, 1997.

S. Kumar, J. Ghosh, and M. M. Crawford, Hierarchical Fusion of Multiple Classifiers for Hyperspectral Data Analysis, Pattern Analysis & Applications, vol.5, issue.2, pp.210-220, 2002.
DOI : 10.1007/s100440200019

L. I. Kuncheva, Using diversity measures for generating error-correcting output codes in classifier ensembles, Pattern Recognition Letters, vol.26, issue.1, pp.83-90, 2005.
DOI : 10.1016/j.patrec.2004.08.019

L. I. Kuncheva and W. J. , Pca feature extraction for change detection in multidimensional unlabelled streaming data, 21st International Conference on Pattern Recognition, 2012.

L. Lam and C. Y. Suen, Optimal combinations of pattern classifiers, Pattern Recognition Letters, vol.16, issue.9, pp.945-954, 1995.
DOI : 10.1016/0167-8655(95)00050-Q

T. Liao, Classification of weld flaws with imbalanced class data, Expert Systems with Applications, vol.35, issue.3, pp.1041-1052, 2008.
DOI : 10.1016/j.eswa.2007.08.044

H. Liu and H. Motoda, On issues of instance selection, Data Mining and Knowledge Discovery, vol.6, issue.2, pp.115-130, 2002.
DOI : 10.1023/A:1014056429969

X. Liu, J. Wu, and Z. Zhou, Exploratory undersampling for class-imbalance learning. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol.39, issue.2, pp.539-550, 2009.

D. Mackay, The Epistemological Problem for Automata, Automata Studies, 1956.
DOI : 10.1515/9781400882618-012

L. M. Manevitz and M. Yousef, One-class svms for document classification, The Journal of Machine Learning Research, vol.2, pp.139-154, 2002.

D. Marr, A theory for cerebral neocortex, Proceedings of the Royal Society of London. Series B, Biological Sciences, pp.161-234, 1970.

F. J. Martínez-estudillo, P. A. Gutiérrez, C. Hervás, and J. Fernández, Evolutionary learning by a sensitivity-accuracy approach for multi-class problems, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp.1581-1588, 2008.
DOI : 10.1109/CEC.2008.4631003

U. Müller-funk, F. Pukelsheim, and H. Witting, On the Attainment of the Cramer-Rao Bound in $\mathbb{L}_r$-Differentiable Families of Distributions, The Annals of Statistics, vol.17, issue.4, pp.1742-1748, 1989.
DOI : 10.1214/aos/1176347392

R. Nock and F. Nielsen, On the efficient minimization of classification-calibrated surrogates, NIPS*21, pp.1201-1208, 2008.

R. Nock and F. Nielsen, Bregman Divergences and Surrogates for Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.11, pp.2048-2059, 2009.
DOI : 10.1109/TPAMI.2008.225

R. Nock and F. Nielsen, Information-geometric lenses for multiple foci+contexts interfaces, SIGGRAPH Asia 2013 Technical Briefs on, SA '13, 2013.
DOI : 10.1145/2542355.2542378

R. Nock, F. Nielsen, and E. Briys, Non-linear book manifolds, Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries, JCDL '13, pp.313-322, 2013.
DOI : 10.1145/2467696.2467697

R. Nock, P. Piro, F. Nielsen, W. B. Ali, and M. Barlaud, Boosting k-NN for Categorization of Natural Scenes, International Journal of Computer Vision, vol.11, issue.3???4, pp.294-314, 2012.
DOI : 10.1007/s11263-012-0539-2
URL : https://hal.archives-ouvertes.fr/hal-00481712

F. Perronnin, Z. Akata, Z. Harchaoui, and C. Schmid, Towards good practice in large-scale learning for image classification, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.3482-3489, 2012.
DOI : 10.1109/CVPR.2012.6248090
URL : https://hal.archives-ouvertes.fr/hal-00690014

C. Phua, D. Alahakoon, and V. Lee, Minority report in fraud detection, ACM SIGKDD Explorations Newsletter, vol.6, issue.1, pp.50-59, 2004.
DOI : 10.1145/1007730.1007738

P. Piro, R. Nock, F. Nielsen, and M. Barlaud, Leveraging k-NN for generic classification boosting, Neurocomputing, vol.80, pp.3-9, 2012.
DOI : 10.1016/j.neucom.2011.07.026
URL : https://hal.archives-ouvertes.fr/hal-00664462

W. B. Piro, L. Crescence, D. Giampaglia, O. Ferhat, J. Darcourt et al., A bio-inspired learning and classification method for subcellular localization of a plasma membrane protein, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00959309

J. Platt, Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods, pp.61-74, 1999.

F. Provost, Machine learning from imbalanced data sets 101, Proceedings of the AAAI 2000 workshop on imbalanced data sets, 2000.

J. R. Quinlan, C4.5 : programs for machine learning, 1993.

P. Riddle, R. Segal, and O. Etzioni, REPRESENTATION DESIGN AND BRUTE-FORCE INDUCTION IN A BOEING MANUFACTURING DOMAIN, Applied Artificial Intelligence, vol.2, issue.1, pp.125-147, 1994.
DOI : 10.1016/0004-3702(90)90037-Z

R. Rifkin and A. Klautau, In defense of one-vs-all classification, The Journal of Machine Learning Research, vol.5, pp.101-141, 2004.

A. Rigon, P. Soda, D. Zennaro, G. Iannello, and A. Afeltra, Indirect immunofluorescence in autoimmune diseases: Assessment of digital images for diagnostic purpose, Cytometry Part B: Clinical Cytometry, vol.14, issue.6, pp.472-477, 2007.
DOI : 10.1002/cyto.b.20356

C. Sansone, F. Tortorella, and M. Vento, A CLASSIFICATION RELIABILITY DRIVEN REJECT RULE FOR MULTI-EXPERT SYSTEMS, International Journal of Pattern Recognition and Artificial Intelligence, vol.15, issue.06, pp.885-904, 2001.
DOI : 10.1142/S0218001401001210

R. E. Schapire, Y. Freund, P. Bartlett, and W. S. Lee, Boosting the margin: a new explanation for the effectiveness of voting methods, The Annals of Statistics, vol.26, issue.5, pp.1651-1686, 1998.
DOI : 10.1214/aos/1024691352

R. E. Schapire and Y. Singer, Improved boosting algorithms using confidence-rated predictions, Proceedings of the eleventh annual conference on Computational learning theory , COLT' 98, pp.297-336, 1999.
DOI : 10.1145/279943.279960

F. Sebastiani, Machine learning in automated text categorization, ACM Computing Surveys, vol.34, issue.1, pp.1-47, 2002.
DOI : 10.1145/505282.505283

M. Sebban, R. Nock, and S. Lallich, Boosting Neighborhood-Based Classifiers, Proc. of the 18 th International Conference on Machine Learning, pp.505-512, 2001.

M. Sebban, R. Nock, and S. Lallich, Stopping criterion for boosting-based data reduction techniques: from binary to multiclass problems, J. of Mach. Learn. Res, vol.3, pp.863-885, 2003.

B. Selman, H. Levesque, and D. Mitchell, A new method for solving hard satisfiability problems, Proceedings of the tenth national conference on Artificial intelligence, pp.440-446, 1992.

S. Shalev-shwartz, Y. Singer, and N. Srebro, Pegasos, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.807-814, 2007.
DOI : 10.1145/1273496.1273598

J. G. Shanahan and N. Roma, Boosting support vector machines for text classification through parameter-free threshold relaxation, Proceedings of the twelfth international conference on Information and knowledge management , CIKM '03, pp.247-254, 2003.
DOI : 10.1145/956863.956911

Y. Shiraishi and K. Fukumizu, Statistical approaches to combining binary classifiers for multi-class classification, Neurocomputing, vol.74, issue.5, pp.680-688, 2011.
DOI : 10.1016/j.neucom.2010.09.004

P. Simeone, C. Marrocco, and F. Tortorella, Design of reject rules for ECOC classification systems, Pattern Recognition, vol.45, issue.2, pp.863-875, 2012.
DOI : 10.1016/j.patcog.2011.08.001

P. Soda, Experiments on solving multiclass recognition tasks in the biological and medical domains, IEEE International Conference on Bio-inspired Systems and Signal Processing, pp.64-71, 2008.

P. Soda, A multi-objective optimisation approach for class imbalance learning, Pattern Recognition, vol.44, issue.8, pp.1801-1810, 2011.
DOI : 10.1016/j.patcog.2011.01.015

P. Soda and G. Iannello, Decomposition Methods and Learning Approaches for Imbalanced Dataset: An Experimental Integration, 2010 20th International Conference on Pattern Recognition, pp.3117-3120, 2010.
DOI : 10.1109/ICPR.2010.763

P. Soda, G. Iannello, and M. Vento, A multiple expert system for classifying fluorescent intensity in antinuclear autoantibodies analysis, Pattern Analysis and Applications, vol.33, issue.3, pp.215-226, 2009.
DOI : 10.1007/s10044-008-0116-z

J. Stefanowski, Multiple and hybrid classifiers, pp.174-188, 2001.

C. Stone, Consistent Nonparametric Regression, The Annals of Statistics, vol.5, issue.4, pp.595-645, 1977.
DOI : 10.1214/aos/1176343886
URL : http://projecteuclid.org/download/pdf_1/euclid.aos/1176343886

A. Sun, E. Lim, B. Benatallah, and M. Hassan, FISA: Feature-Based Instance Selection for Imbalanced Text Classification, Advances in Knowledge Discovery and Data Mining, pp.250-254, 2006.
DOI : 10.1007/11731139_30

A. Sun, E. Lim, and Y. Liu, On strategies for imbalanced text classification using SVM: A comparative study, Decision Support Systems, vol.48, issue.1, pp.191-201, 2009.
DOI : 10.1016/j.dss.2009.07.011

A. Sun, E. Lim, W. Ng, and J. Srivastava, Blocking reduction strategies in hierarchical text classification. Knowledge and Data Engineering, IEEE Transactions on, vol.16, issue.10, pp.1305-1308, 2004.

Y. Sun, M. S. Kamel, A. K. Wong, and Y. Wang, Cost-sensitive boosting for classification of imbalanced data, Pattern Recognition, vol.40, issue.12, pp.403358-3378, 2007.
DOI : 10.1016/j.patcog.2007.04.009

R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, IEEE Transactions on Neural Networks, vol.9, issue.5, 1998.
DOI : 10.1109/TNN.1998.712192

A. Tan, D. Gilbert, and Y. Deville, Multi-class protein fold classification using a new ensemble machine learning approach, Genome Informatics, vol.14, pp.206-217, 2003.

E. Vernet, R. Williamson, and M. Reid, Composite multiclass losses, pp.1224-1232, 2011.

K. Veropoulos, C. Campbell, and N. Cristianini, Controlling the sensitivity of support vector machines, Proceedings of the international joint conference on artificial intelligence, pp.55-60, 1999.

J. Wang, J. Yand, K. Yu, F. Lv, T. Huang et al., Locality-constrained Linear Coding for image classification, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.3360-3367, 2010.
DOI : 10.1109/CVPR.2010.5540018

S. Wang and X. Yao, Multiclass Imbalance Problems: Analysis and Potential Solutions, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol.42, issue.4, pp.1119-1130, 2012.
DOI : 10.1109/TSMCB.2012.2187280

G. M. Weiss, Mining with rarity, ACM SIGKDD Explorations Newsletter, vol.6, issue.1, pp.7-19, 2004.
DOI : 10.1145/1007730.1007734

G. M. Weiss and F. Provost, Learning when training data are costly: the effect of class distribution on tree induction, Journal of Artificial Intelligence Research, vol.19, pp.315-354, 2003.

D. L. Wilson, Asymptotic properties of nearest neighbor rules using edited data. Systems, Man and Cybernetics, IEEE Transactions on, issue.3, pp.408-421, 1972.

T. Windeatt and R. Ghaderi, Binary labelling and decision-level fusion, Information Fusion, vol.2, issue.2, pp.103-112, 2001.
DOI : 10.1016/S1566-2535(01)00029-X

G. Wu and E. Y. Chang, Class-boundary alignment for imbalanced dataset learning, ICML 2003 workshop on learning from imbalanced data sets II, pp.49-56, 2003.

G. Wu and E. Y. Chang, Kba: Kernel boundary alignment considering imbalanced data distribution . Knowledge and Data Engineering, IEEE Transactions on, vol.17, issue.6, pp.786-795, 2005.

J. Wu, H. Xiong, P. Wu, and J. Chen, Local decomposition for rare class analysis, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '07, pp.814-823, 2007.
DOI : 10.1145/1281192.1281279

M. Wu and J. Ye, A small sphere and large margin approach for novelty detection using training data with outliers. Pattern Analysis and Machine Intelligence, IEEE Transactions on, issue.11, pp.312088-2092, 2009.

T. Wu, C. Lin, and R. Weng, Probability estimates for multi-class classification by pairwise coupling, J. of Mach. Learn. Res, vol.5, pp.975-1005, 2003.

J. Xiao, J. Hays, K. Ehringer, A. Oliva, and A. Torralba, SUN database: Large-scale scene recognition from abbey to zoo, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.3485-3492, 2010.
DOI : 10.1109/CVPR.2010.5539970

L. Xu, A. Krzyzak, and C. Suen, Methods of combining multiple classifiers and their applications to handwriting recognition. Systems, Man and Cybernetics, IEEE Transactions on, vol.22, issue.3, pp.418-435, 1992.

R. Yan, Y. Liu, R. Jin, and A. Hauptmann, On predicting rare classes with svm ensembles in scene classification, Acoustics, Speech, and Signal Processing Proceedings .(ICASSP'03). 2003 IEEE International Conference on, p.21, 2003.

Y. Yang, A study of thresholding strategies for text categorization, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '01, pp.137-145, 2001.
DOI : 10.1145/383952.383975

X. Ye, M. Cheriet, and C. Y. Suen, StrCombo: combination of string recognizers, Pattern Recognition Letters, vol.23, issue.4, pp.381-394, 2002.
DOI : 10.1016/S0167-8655(01)00171-4

B. Zadrozny and C. Elkan, Transforming classifier scores into accurate multiclass probability estimates, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '02, pp.694-699, 2002.
DOI : 10.1145/775047.775151

X. Zhao, X. Li, L. Chen, and K. Aihara, Protein classification with imbalanced data, Proteins: Structure, Function, and Bioinformatics, vol.33, issue.Database issue, pp.1125-1132, 2008.
DOI : 10.1002/prot.21870

Z. Zhou and X. Liu, Training cost-sensitive neural networks with methods addressing the class imbalance problem, IEEE Transactions on Knowledge and Data Engineering, vol.18, issue.1, pp.63-77, 2006.
DOI : 10.1109/TKDE.2006.17