J. Abellan and A. R. Masegosa, An Experimental Study about Simple Decision Trees for Bagging Ensemble on Datasets with Classification Noise, Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pp.446-456, 2009.
DOI : 10.2307/3001968

O. S. Ahmed, S. E. Franklin, M. A. Wulder, and J. White, Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm, ISPRS Journal of Photogrammetry and Remote Sensing, vol.101, pp.89-101, 2015.
DOI : 10.1016/j.isprsjprs.2014.11.007

F. Aiolli and A. Sperduti, A re-weighting strategy for improving margins, Artificial Intelligence, vol.137, issue.1-2, pp.197-216, 2002.
DOI : 10.1016/S0004-3702(02)00122-4

R. Akbani, S. Kwek, and N. Japkowicz, Applying Support Vector Machines to Imbalanced Datasets, pp.39-50, 2004.
DOI : 10.1007/978-3-540-30115-8_7

URL : http://www.cs.utsa.edu/~rakbani/publications/Akbani-SVM-ECML04.pdf

K. Ali and M. Pazzani, Error reduction through learning multiple descriptions, Machine Learning, pp.173-202, 1996.
DOI : 10.1016/B978-0-444-88650-7.50030-5

URL : https://link.springer.com/content/pdf/10.1007%2FBF00058611.pdf

M. Alshawabkeh, Hypothesis margin based weighting for feature selection using boosting: theory, algorithms and applications, 2013.

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

R. Banfield, L. Hall, K. Bowyer, and W. Kegelmeyer, Ensemble diversity measures and their application to thinning, Information Fusion, pp.49-62, 2005.

R. Barandela and E. Gasca, Decontamination of Training Samples for Supervised Pattern Recognition Methods, Advances in Pattern Recognition, pp.621-630, 2000.
DOI : 10.1007/3-540-44522-6_64

R. Barandela, J. S. Sánchez, and R. M. Valdovinos, New Applications of Ensembles of Classifiers, Pattern Analysis & Applications, vol.6, issue.3, pp.245-256, 2003.
DOI : 10.1007/s10044-003-0192-z

P. L. Bartlett, M. I. 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

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

E. Bauer and R. Kohavi, An empirical comparison of voting classification algorithms: Bagging, boosting, and variants, Machine Learning, pp.105-139, 1999.

B. Beguet, S. Boukir, D. Guyon, and N. Chehata, Modelling-Based Feature Selection for Classification of Forest Structure Using Very High Resolution Multispectral Imagery, 2013 IEEE International Conference on Systems, Man, and Cybernetics, pp.4294-4299
DOI : 10.1109/SMC.2013.732

B. Bhasuran, G. Murugesan, S. Abdulkadhar, and J. Natarajan, Stacked ensemble combined with fuzzy matching for biomedical named entity recognition of diseases, Journal of Biomedical Informatics, vol.64, pp.1-9, 2016.
DOI : 10.1016/j.jbi.2016.09.009

G. Biau, Analysis of a random forests model, J. Mach. Learn. Res, vol.13, pp.1063-1095, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00476545

J. Blaszczy?ski and J. Stefanowski, Neighbourhood sampling in bagging for imbalanced data, Neurocomputing, vol.150, pp.529-542, 2015.
DOI : 10.1016/j.neucom.2014.07.064

S. Boukir, L. Guo, and N. Chehata, Classification of remote sensing data using margin-based ensemble methods, 2013 IEEE International Conference on Image Processing, pp.2602-2606, 2013.
DOI : 10.1109/ICIP.2013.6738536

S. Boukir, O. Regniers, L. Guo, L. Bombrun, and C. Germain, Texturebased forest cover classification using random forests and ensemble margin, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp.3072-3075, 2015.
DOI : 10.1109/igarss.2015.7326465

URL : https://hal.archives-ouvertes.fr/hal-01188174

L. Breiman, Bias, variance, and arcing classifiers, 1996.

L. Breiman, Out-of-bag estimation, tech. report, 1996.

L. Breiman, Arcing the edge, tech. report, Statist, 1997.

L. Breiman, Randomizing outputs to increase prediction accuracy, Machine Learning, pp.229-242, 2000.

L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees, 1984.

C. Brodley and M. Friedl, Improving automated land cover mapping by identifying and eliminating mislabeled observations from training data, IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium, pp.1379-1381, 1996.
DOI : 10.1109/IGARSS.1996.516669

C. E. Brodley and M. A. Friedl, Identifying mislabeled training data, Journal of artificial intelligence research, vol.11, pp.131-167, 1999.

C. J. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, vol.2, issue.2, pp.121-167, 1998.
DOI : 10.1023/A:1009715923555

J. B. Campbell and R. H. Wynne, Introduction to remote sensing, Geocarto International, vol.2, issue.4, 2011.
DOI : 10.1080/10106048709354126

I. Cantador and J. Dorronsoro, Boosting Parallel Perceptrons for Label Noise Reduction in Classification Problems, Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach, pp.586-593, 2005.
DOI : 10.1007/11499305_60

J. J. Cao, S. Kwong, and R. Wang, A noise-detection based AdaBoost algorithm for mislabeled data, Pattern Recognition, vol.45, issue.12, pp.4451-4465, 2012.
DOI : 10.1016/j.patcog.2012.05.002

C. Catal, O. Alan, and K. Balkan, Class noise detection based on software metrics and ROC curves, Information Sciences, vol.181, issue.21, pp.4867-4877, 2011.
DOI : 10.1016/j.ins.2011.06.017

G. D. Cavalcanti, L. S. Oliveira, T. J. Moura, and G. V. Carvalho, Combining diversity measures for ensemble pruning, Pattern Recognition Letters, vol.74, pp.74-112, 2016.
DOI : 10.1016/j.patrec.2016.01.029

J. C. Chan and D. Paelinckx, Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery, Remote Sensing of Environment, vol.112, issue.6, pp.2999-3011, 2008.
DOI : 10.1016/j.rse.2008.02.011

N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, Smote: Synthetic minority over-sampling technique, J. Artif. Int. Res, vol.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

C. Chen, A. Liaw, and L. Breiman, Using random forest to learn imbalanced data, 2004.

C. H. Chen and P. P. Ho, Statistical pattern recognition in remote sensing, Pattern Recognition, vol.41, issue.9, pp.2731-2741, 2008.
DOI : 10.1016/j.patcog.2008.04.013

J. Chen and H. Yu, Unsupervised ensemble ranking of terms in electronic health record notes based on their importance to patients, Journal of Biomedical Informatics, vol.68, pp.121-131, 2017.
DOI : 10.1016/j.jbi.2017.02.016

W. W. Cohen, Fast Effective Rule Induction, Twelfth International Conference on Machine Learning, pp.115-123, 1995.
DOI : 10.1016/B978-1-55860-377-6.50023-2

M. Condorcet, Essay on the application of analysis to the probability of majority decisions, 1785.

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

URL : http://ssg.mit.edu/cal/abs/2000_spring/np_dens/classification/cover67.pdf

M. J. Cracknell and A. M. Reading, Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information, Computers & Geosciences, vol.63, pp.63-85, 2014.
DOI : 10.1016/j.cageo.2013.10.008

K. Crammer, R. Gilad-bachrach, A. Navot, and N. Tishby, Margin analysis of the lvq algorithm, Advances in Neural Information Processing Systems, pp.462-469, 2002.

P. Cunningham and J. Carney, Diversity versus Quality in Classification Ensembles Based on Feature Selection, 11th European Conference on Machine Learning, pp.109-116, 2000.
DOI : 10.1007/3-540-45164-1_12

S. J. Delany and P. Cunningham, An Analysis of Case-Base Editing in a Spam Filtering System, Advances in Case-Based Reasoning, pp.128-141, 2004.
DOI : 10.1007/978-3-540-28631-8_11

I. S. Dhillon, Y. Guan, and B. Kulis, Kernel k-means, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, pp.551-556, 2004.
DOI : 10.1145/1014052.1014118

T. Dietterich, An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization, Machine Learning, pp.139-157, 2000.

J. Díez-pastor, J. Rodríguez, C. García-osorio, and L. I. Kuncheva, Random balance: Ensembles of variable priors classifiers for imbalanced data, Knowledge-Based Systems, pp.96-111, 2015.

C. Domingo and O. Watanabe, Madaboost: A modification of adaboost, Proceedings of the Thirteenth Annual Conference on Computational Learning Theory, pp.180-189, 2000.

P. Domingos, A unified bias-variance decomposition for zero-one and squared loss, AAAI/IAAI(Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence), pp.564-569, 2000.

P. Domingos and M. Pazzani, On the optimality of the simple bayesian classifier under zero-one loss, Machine Learning, vol.29, issue.2/3, pp.103-130, 1997.
DOI : 10.1023/A:1007413511361

P. Du, A. Samat, B. Waske, S. Liu, and Z. Li, Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features, ISPRS Journal of Photogrammetry and Remote Sensing, vol.105, pp.38-53, 2015.
DOI : 10.1016/j.isprsjprs.2015.03.002

P. Du, J. Xia, J. Chanussot, and X. He, Hyperspectral remote sensing image classification based on the integration of support vector machine and random forest, 2012 IEEE International Geoscience and Remote Sensing Symposium, pp.174-177, 2012.
DOI : 10.1109/IGARSS.2012.6351609

URL : https://hal.archives-ouvertes.fr/hal-00799693

P. Du, J. Xia, W. Zhang, K. Tan, Y. Liu et al., Multiple Classifier System for Remote Sensing Image Classification: A Review, Sensors, vol.33, issue.12, pp.12-4764, 2012.
DOI : 10.1016/j.rse.2004.07.013

R. Duda, P. Hart, and D. Stork, Pattern Classification, 2001.

B. Efron and R. Tibshirani, Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy, Statistical Science, vol.1, issue.1, pp.54-75, 1986.
DOI : 10.1214/ss/1177013815

S. Ertekin, J. Huang, L. Bottou, and C. L. Giles, Learning on the border, Proceedings of the sixteenth ACM conference on Conference on information and knowledge management , CIKM '07, pp.127-136, 2007.
DOI : 10.1145/1321440.1321461

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

X. N. Fan, K. Tang, and T. Weise, Margin-Based Over-Sampling Method for Learning from Imbalanced Datasets, Advances in Knowledge Discovery and Data Mining, pp.309-320, 2011.
DOI : 10.1109/TSMCB.2008.2007853

A. Fernández, S. García, and F. Herrera, Addressing the Classification with Imbalanced Data: Open Problems and New Challenges on Class Distribution, pp.1-10, 2011.
DOI : 10.1016/j.ins.2010.09.018

A. Fernández, V. López, M. Galar, M. J. Jesus, and F. Herrera, Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches, Knowledge-Based Systems, pp.42-97, 2013.

A. E. Ferreira and D. Alarcao, Real-time blind source separation system with applications to distant speech recognition, Applied Acoustics, vol.113, pp.170-184, 2016.
DOI : 10.1016/j.apacoust.2016.06.024

B. Frenay and M. Verleysen, Classification in the Presence of Label Noise: A Survey, IEEE Transactions on Neural Networks and Learning Systems, vol.25, issue.5, pp.845-869, 2014.
DOI : 10.1109/TNNLS.2013.2292894

Y. Freund and R. Schapire, Experiments with a new boosting algorithm, The 13th International Conference on Machine Learning, ICML'96, pp.148-156, 1996.

Y. Freund and R. E. Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Journal of Computer and System Sciences, vol.55, issue.1, pp.55-119, 1997.
DOI : 10.1006/jcss.1997.1504

D. G. Friedman and N. Geiger, Bayesian network classifiers, Machine Learning, vol.29, issue.2/3, pp.131-163, 1997.
DOI : 10.1023/A:1007465528199

J. H. Friedman and P. Hall, On bagging and nonlinear estimation, Journal of Statistical Planning and Inference, vol.137, issue.3, pp.669-683, 2007.
DOI : 10.1016/j.jspi.2006.06.002

M. Galar, A. Fernandez, E. Barrenechea, H. Bustince, and F. Herrera, A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol.42, issue.4, pp.42-463, 2012.
DOI : 10.1109/TSMCC.2011.2161285

M. Galar, A. Fernández, E. Barrenechea, and F. Herrera, EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling, Pattern Recognition, vol.46, issue.12, pp.46-3460, 2013.
DOI : 10.1016/j.patcog.2013.05.006

D. Gamberger, N. Lavrac, and S. Dzeroski, Noise detection and elimination in data preprocessing: Experiments in medical domains, Applied Artificial Intelligence, vol.14, issue.2, pp.205-223, 2000.
DOI : 10.1080/088395100117124

W. Gao and Z. H. Zhou, The kth, median and average margin bounds for adaboost, CoRR (Computing Research Repository), abs/1009, p.3613, 2010.

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

S. Garcia, J. Luengo, and F. Herrera, Dealing with Noisy Data, Data Preprocessing in Data Mining, pp.107-145, 2015.
DOI : 10.1007/978-3-319-10247-4_5

U. Gessner, M. Machwitz, C. Conrad, and S. Dech, Estimating the fractional cover of growth forms and bare surface in savannas. A multi-resolution approach based on regression tree ensembles, Remote Sensing of Environment, vol.129, pp.90-102, 2013.
DOI : 10.1016/j.rse.2012.10.026

P. Gislason, J. Benediktsson, and J. Sveinsson, Random Forests for land cover classification, Pattern Recognition Letters, vol.27, issue.4, pp.294-300, 2006.
DOI : 10.1016/j.patrec.2005.08.011

S. Gu and Y. Jin, Multi-train: A semi-supervised heterogeneous ensemble classifier, Neurocomputing, vol.249, pp.249-202, 2017.
DOI : 10.1016/j.neucom.2017.03.063

L. Guo, Margin framework for ensemble classifiers. Application to remote sensing data, 2011.

L. Guo and S. Boukir, Margin-based ordered aggregation for ensemble pruning, Pattern Recognition Letters, vol.34, issue.6, pp.603-609, 2013.
DOI : 10.1016/j.patrec.2013.01.003

L. Guo, S. Boukir, and N. Chehata, Support Vectors Selection for Supervised Learning Using an Ensemble Approach, 2010 20th International Conference on Pattern Recognition, pp.37-40, 2010.
DOI : 10.1109/ICPR.2010.18

L. Guo, N. Chehata, C. Mallet, and S. Boukir, Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests, ISPRS Journal of Photogrammetry and Remote Sensing, vol.66, issue.1, pp.66-56, 2011.
DOI : 10.1016/j.isprsjprs.2010.08.007

I. Guyon, N. Matic, and V. Vapnik, Advances in knowledge discovery and data mining, American Association for Artificial Intelligence, ch. Discovering Informative Patterns and Data Cleaning, pp.181-203, 1996.

M. Han, X. R. Zhu, and W. Yao, Remote sensing image classification based on neural network ensemble algorithm, Neurocomputing, vol.78, issue.1, pp.78-133, 2012.
DOI : 10.1016/j.neucom.2011.04.044

S. Hariharan, S. Dhanasekar, and K. Desikan, Reachability Based Web Page Ranking Using Wavelets, Procedia Computer Science, vol.50, pp.157-162, 2015.
DOI : 10.1016/j.procs.2015.04.078

I. K. Haryana, V. N. Fikriyah, and N. V. Yulianti, Application of remote sensing and geographic information system for settlement land use classification planning in bantul based on earthquake disaster mitigation (case study: Bantul earthquake, Procedia Environmental Sciences, pp.17-434, 2006.

T. Hastie and G. E. Batista, Classification by pairwise coupling, The Annals of Statistics, vol.26, issue.2, pp.451-471, 1998.
DOI : 10.1214/aos/1028144844

S. Haykin, Neural Networks: A Comprehensive Foundation, 1998.

H. He and Y. Ma, Imbalanced learning: foundations, algorithms, and applications, 2013.
DOI : 10.1002/9781118646106

H. B. He and E. A. Garcia, Learning from imbalanced data, IEEE Transactions on Knowledge and Data Engineering, vol.21, pp.1263-1284, 2009.

M. A. Hernandez and S. J. Stolfo, Real-world data is dirty: Data cleansing and the merge/purge problem, Data Mining and Knowledge Discovery, vol.2, issue.1, pp.9-37, 1998.
DOI : 10.1023/A:1009761603038

S. Hido, H. Kashima, and Y. Takahashi, Roughly balanced bagging for imbalanced data, Statistical Analysis and Data Mining, vol.45, issue.1, pp.412-426, 2009.
DOI : 10.1002/sam.10061

T. K. Ho, The random subspace method for constructing decision forests, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, pp.832-844, 1998.

R. C. Holte, L. E. Acker, and B. W. Porter, Concept learning and the problem of small disjuncts, Proceedings of the 11th International Joint Conference on Artificial Intelligence, pp.813-818, 1989.

M. Hosseini, M. Saradjian, A. Javahery, and S. Nadi, Noise removal from land cover maps by post processing of classification results, RAST '07. 3rd International Conference on Recent Advances in Space Technologies, pp.309-314, 2007.

A. V. Hout and P. G. Heijden, Randomized Response, Statistical Disclosure Control and Misclassificatio: a Review, International Statistical Review, vol.146, issue.2, pp.269-288, 2002.
DOI : 10.1007/978-1-4613-0121-9

Q. Hu, L. Li, X. Wu, G. Schaefer, and D. Yu, Exploiting diversity for optimizing margin distribution in ensemble learning, Knowledge-Based Systems, pp.90-104, 2014.

S. G. Hu, Y. F. Liang, L. T. Ma, and Y. He, MSMOTE: Improving Classification Performance When Training Data is Imbalanced, 2009 Second International Workshop on Computer Science and Engineering, pp.13-17, 2009.
DOI : 10.1109/WCSE.2009.756

N. Hughes, S. Roberts, and L. Tarassenko, Semi-supervised learning of probabilistic models for ECG segmentation, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.434-437, 2004.
DOI : 10.1109/IEMBS.2004.1403187

J. J. Blaszczy?ski, L. Stefanowski, and . Idkowiak, Extending Bagging for Imbalanced Data, Proceeding of the eighth CORES (Core Ordering and Reporting Enterprise System), Springer Series on Advances in Intelligent Systems and Computing, pp.269-278, 2013.
DOI : 10.1007/978-3-319-00969-8_26

S. E. Morgan and N. C. Coops, Aerial Photography: A Rapidly Evolving Tool for Ecological Management, BioScience, vol.60, issue.1, pp.47-59, 2010.
DOI : 10.1525/bio.2010.60.1.9

N. Japkowicz and S. Stephen, The class imbalance problem: A systematic study, Intelligence Data Analysis, vol.6, pp.429-449, 2002.

R. Jin and J. Zhang, Multi-Class Learning by Smoothed Boosting, Machine Learning, vol.4, issue.4, pp.207-227, 2007.
DOI : 10.1023/A:1008663629662

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

G. John, Robust decision trees: Removing outliers from databases, First International Conference on Knowledge Discovery and Data Mining, pp.174-179, 1995.

B. A. Johnson and K. Iizuka, Integrating OpenStreetMap crowdsourced data and Landsat time-series imagery for rapid land use/land cover (LULC) mapping: Case study of the Laguna de Bay area of the Philippines, Applied Geography, vol.67, pp.140-149, 2016.
DOI : 10.1016/j.apgeog.2015.12.006

M. Kapp, R. Sabourin, and P. Maupin, An empirical study on diversity measures and margin theory for ensembles of classifiers, 2007 10th International Conference on Information Fusion, pp.1-8, 2007.
DOI : 10.1109/ICIF.2007.4408144

A. Karmaker and S. Kwek, A boosting approach to remove class label noise, Fifth International Conference on Hybrid Intelligent Systems (HIS'05), pp.169-177, 2005.
DOI : 10.1109/ICHIS.2005.1

T. M. Khoshgoftaar, A. Fazelpour, D. J. Dittman, and A. Napolitano, Ensemble vs. Data Sampling: Which Option Is Best Suited to Improve Classification Performance of Imbalanced Bioinformatics Data?, 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), pp.705-712, 2015.
DOI : 10.1109/ICTAI.2015.106

T. M. Khoshgoftaar, J. V. Hulse, and A. Napolitano, Comparing Boosting and Bagging Techniques With Noisy and Imbalanced Data, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol.41, issue.3, pp.41-552, 2011.
DOI : 10.1109/TSMCA.2010.2084081

T. M. Khoshgoftaar, S. Zhong, and V. Joshi, Enhancing software quality estimation using ensemble-classifier based noise filtering, Intell. Data Anal, vol.9, pp.3-27, 2005.

Y. Kim, Averaged Boosting: A Noise-Robust Ensemble Method, Advances in Knowledge Discovery and Data Mining, pp.388-393, 2003.
DOI : 10.1007/3-540-36175-8_38

S. Kluckner and H. Bischof, Semantic classification by covariance descriptors within a randomized forest, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pp.665-672, 2009.
DOI : 10.1109/ICCVW.2009.5457638

B. Krawczyk, Learning from imbalanced data: open challenges and future directions, Progress in Artificial Intelligence, vol.25, issue.1, pp.1-12, 2016.
DOI : 10.1109/TNNLS.2012.2236570

URL : https://doi.org/10.1007/s13748-016-0094-0

A. Krieger, C. Long, and A. Wyner, Boosting noisy data, Proceedings of the Eighteenth International Conference on Machine Learning, ICML '01, pp.274-281, 2001.

L. I. Kuncheva and C. J. Whitaker, Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy, Machine Learning, pp.181-207, 2003.

B. and L. Saux, Interactive Design of Object Classifiers in Remote Sensing, 2014 22nd International Conference on Pattern Recognition, pp.2572-2577, 2014.
DOI : 10.1109/ICPR.2014.444

URL : https://hal.archives-ouvertes.fr/hal-01070388

C. Li, B. Kuo, C. Lin, and C. Huang, A Spatial???Contextual Support Vector Machine for Remotely Sensed Image Classification, IEEE Transactions on Geoscience and Remote Sensing, vol.50, issue.3, pp.50-784, 2012.
DOI : 10.1109/TGRS.2011.2162246

L. Li, A. Pratap, H. T. Lin, and Y. S. Abu-mostafa, Improving Generalization by Data Categorization, Knowledge Discovery in Databases: PKDD 2005, pp.157-168, 2005.
DOI : 10.1007/11564126_19

L. J. Li, B. Zou, Q. H. Hu, X. Q. Wu, and D. R. Yu, Dynamic classifier ensemble using classification confidence, Neurocomputing, vol.99, pp.99-581, 2013.
DOI : 10.1016/j.neucom.2012.07.026

Y. Lin, Y. Lee, and G. Wahba, Support vector machines for classification in nonstandard situations, Machine Learning, pp.191-202, 2002.

C. X. Ling and V. S. Sheng, Cost-sensitive Learning and the Class Imbalanced Problem, Sammut C (ed) Encyclopedia of machine learning, 2008.

F. T. Liu, K. M. Ting, Y. Yu, and Z. Zhou, Spectrum of variable-random trees, J. Artif. Int. Res, vol.32, pp.355-384, 2008.

T. Y. Liu, EasyEnsemble and Feature Selection for Imbalance Data Sets, 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, pp.517-520, 2009.
DOI : 10.1109/IJCBS.2009.22

X. Y. Liu and Z. H. Zhou, Ensemble Methods for Class Imbalance Learning, Imbalanced Learning: Foundations, Algorithms,and Applications, pp.61-82, 2013.
DOI : 10.1016/S0031-3203(96)00142-2

Y. H. Liu and Y. T. Chen, Total margin based adaptive fuzzy support vector machines for multiview face recognition, 2005 IEEE International Conference on Systems, Man and Cybernetics, pp.1704-1711, 2005.

V. López, A. Fernández, S. Garcia, V. Palade, and F. Herrera, An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics, Information Sciences, vol.250, pp.113-141, 2013.
DOI : 10.1016/j.ins.2013.07.007

K. Lowell, P. Woodgate, G. Richards, S. Jones, and L. Buxton, Fuzzy Reliability Assessment of Multi-Period Land-cover Change Maps, Photogrammetric Engineering & Remote Sensing, vol.71, issue.8, pp.71-939, 2005.
DOI : 10.14358/PERS.71.8.939

O. Loyola-gonzález, J. F. Martínez-trinidad, J. A. Carrasco-ochoa, and M. García-borroto, Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databases, Neurocomputing, vol.175, pp.935-947, 2016.
DOI : 10.1016/j.neucom.2015.04.120

E. Marchiori, Class Conditional Nearest Neighbor for Large Margin Instance Selection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.2, pp.364-370, 2010.
DOI : 10.1109/TPAMI.2009.164

G. Martinez-munoz and A. Suarez, Pruning in ordered bagging ensembles, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.609-616, 2006.
DOI : 10.1145/1143844.1143921

H. Masnadi-shirazi and N. Vasconcelos, On the design of loss functions for classification: theory, robustness to outliers, and savageboost, Advances in Neural Information Processing Systems 21, pp.1049-056, 2009.

P. Mather and B. Tso, Classification Methods for Remotely Sensed Data, 2009.

D. Mease, A. J. Wyner, and A. Buja, Boosted classification trees and class probability/quantile estimation, Journal of Machine Learning Research, vol.8, pp.409-439, 2007.

A. Mellor, S. Boukir, A. Haywood, and S. Jones, Using ensemble margin to explore issues of training data imbalance and mislabeling on large area land cover classification, 2014 IEEE International Conference on Image Processing (ICIP), pp.26-29, 2014.
DOI : 10.1109/ICIP.2014.7026026

A. Mellor, S. Boukir, A. Haywood, and S. Jones, Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin, ISPRS Journal of Photogrammetry and Remote Sensing, vol.105, pp.155-168, 2015.
DOI : 10.1016/j.isprsjprs.2015.03.014

P. Melville and R. J. Mooney, Constructing diverse classifier ensembles using artificial training examples, Proceedings of the 18th International Joint Conference on Artificial Intelligence, IJCAI'03, pp.505-510, 2003.

B. H. Menze, B. M. Kelm, R. Masuch, U. Himmelreich, P. Bachert et al., A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data, BMC Bioinformatics, vol.10, issue.1, pp.10-11, 2009.
DOI : 10.1186/1471-2105-10-213

A. L. Miranda, L. P. Garcia, A. C. Carvalho, and A. C. Lorena, Use of classification algorithms in noise detection and elimination, in Hybrid Artificial Intelligence Systems, pp.417-424, 2009.

T. Munkhdalai, O. Namsrai, and K. H. Ryu, Self-training in significance space of support vectors for imbalanced biomedical event data, BMC Bioinformatics, vol.16, issue.Suppl 7, pp.1-8, 2015.
DOI : 10.1093/bioinformatics/bts332

V. Nikulin, G. J. Mclachlan, and S. K. Ng, Ensemble Approach for the Classification of Imbalanced Data, pp.291-300, 2009.
DOI : 10.1007/978-3-642-10439-8_30

S. Okamoto and Y. Nobuhiro, An average-case analysis of the k-nearest neighbour classifier for noisy domain, Proc. 15th Int. Joint Conf, pp.238-243, 1997.

N. C. Oza, AveBoost2: Boosting for Noisy Data, Multiple Classifier Systems, pp.31-40, 2004.
DOI : 10.1007/978-3-540-25966-4_3

Y. Park and J. Ghosh, Ensembles of $({\alpha})$-Trees for Imbalanced Classification Problems, IEEE Transactions on Knowledge and Data Engineering, vol.26, issue.1, pp.131-143, 2014.
DOI : 10.1109/TKDE.2012.255

M. Pechenizkiy, A. Tsymbal, S. Puuronen, and O. Pechenizkiy, Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction, 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06), pp.708-713, 2006.
DOI : 10.1109/CBMS.2006.65

M. P. Perrone and L. N. Cooper, When networks disagree: Ensemble methods for hybrid neural networks, Artificial Neural Networks for Speech and Vision, pp.126-142, 1993.
DOI : 10.1142/9789812795885_0025

M. Prasad and A. Sowmya, Multi-class unsupervised classification with label correction of HRCT lung images, International Conference on Intelligent Sensing and Information Processing, 2004. Proceedings of, pp.51-56, 2004.
DOI : 10.1109/ICISIP.2004.1287623

Y. Qian, Y. Liang, M. Li, G. Feng, and X. Shi, A resampling ensemble algorithm for classification of imbalance problems, Neurocomputing, vol.143, pp.143-57, 2014.
DOI : 10.1016/j.neucom.2014.06.021

Y. Qian, K. Zhang, and F. Qiu, Spatial contextual noise removal for post classification smoothing of remotely sensed images, Proceedings of the 2005 ACM symposium on Applied computing , SAC '05, pp.524-528, 2005.
DOI : 10.1145/1066677.1066795

J. R. Quinlan, Induction of decision trees, Machine Learning, pp.81-106, 1986.
DOI : 10.1037/13135-000

J. R. Quinlan and C. , 5: Programs for Machine Learning, 1993.

R. Development and C. Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, 2013.

U. Rebbapragada, C. Brodley, D. Sulla-menashe, and M. Friedl, Active Label Correction, 2012 IEEE 12th International Conference on Data Mining, pp.1080-1085, 2012.
DOI : 10.1109/ICDM.2012.162

U. Rebbapragada, R. Lomasky, C. Brodley, and M. Friedl, Generating High-Quality Training Data for Automated Land-Cover Mapping, IGARSS 2008, 2008 IEEE International Geoscience and Remote Sensing Symposium, pp.546-548, 2008.
DOI : 10.1109/IGARSS.2008.4779779

L. Reyzin and R. E. Schapire, How boosting the margin can also boost classifier complexity, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.753-760, 2006.
DOI : 10.1145/1143844.1143939

R. Rifkin and A. Klautau, In defense of one-vs-all classification, J. Mach. Learn. Res, vol.5, pp.101-141, 2004.

S. Río, V. López, J. M. Benítez, and F. Herrera, On the use of mapreduce for imbalanced big data using random forest, Information Sciences, pp.285-112, 2014.

V. Rodriguez-galiano, B. Ghimire, J. Rogan, M. Chica-olmo, and J. Sanchez, An assessment of the effectiveness of a random forest classifier for land-cover classification, ISPRS Journal of Photogrammetry and Remote Sensing, vol.67, pp.67-93, 2012.
DOI : 10.1016/j.isprsjprs.2011.11.002

J. Rogan, J. Franklin, D. Stow, J. Miller, C. Woodcock et al., Mapping land-cover modifications over large areas: A comparison of machine learning algorithms, Remote Sensing of Environment, vol.112, issue.5, pp.2272-2283, 2008.
DOI : 10.1016/j.rse.2007.10.004

L. Rokach, Ensemble-based classifiers, Artificial Intelligence Review, vol.13, issue.4, pp.1-39, 2010.
DOI : 10.1142/5686

M. Sabzevari, G. Martinez-munoz, and A. Suarez, Small margin ensembles can be robust to class-label noise, Neurocomputing, vol.160, 2015.
DOI : 10.1016/j.neucom.2014.12.086

J. A. Saez, M. Galar, J. Luengo, and F. Herrera, Analyzing the presence of noise in multi-class problems: alleviating its influence with the One-vs-One decomposition, Knowledge and Information Systems, vol.22, issue.2, pp.179-206, 2014.
DOI : 10.1007/s10462-004-0751-8

J. A. Sáez, B. Krawczyk, and M. Wo?niak, Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets, Pattern Recognition, vol.57, pp.164-178, 2016.
DOI : 10.1016/j.patcog.2016.03.012

J. A. Saez, J. Luengo, and F. Herrera, Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification, Pattern Recognition, vol.46, issue.1, pp.355-364, 2013.
DOI : 10.1016/j.patcog.2012.07.009

J. A. Sáez, J. Luengo, J. Stefanowski, and F. Herrera, SMOTE???IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering, Information Sciences, vol.291, pp.291-184, 2015.
DOI : 10.1016/j.ins.2014.08.051

R. E. Schapire, The strength of weak learnability, Mach. Learn, vol.5, pp.197-227, 1990.

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.1651-2080, 1998.
DOI : 10.1214/aos/1024691352

R. E. Schapire and Y. Singer, Improved boosting algorithms using confidencerated predictions, Machine Learning, pp.297-336, 1999.

N. Segata, E. Blanzieri, S. Delany, and P. Cunningham, Noise reduction for instance-based learning with a local maximal margin approach, Journal of Intelligent Information Systems, vol.38, issue.3, pp.301-331, 2010.
DOI : 10.1023/A:1007626913721

C. Seiffert, T. M. Khoshgoftaar, J. V. Hulse, and A. Napolitano, RUSBoost: A Hybrid Approach to Alleviating Class Imbalance, Part A: Systems and Humans, pp.40-185, 2010.
DOI : 10.1109/TSMCA.2009.2029559

C. H. Shen and H. X. Li, Boosting Through Optimization of Margin Distributions, IEEE Transactions on Neural Networks, vol.21, issue.4, pp.659-666, 2010.
DOI : 10.1109/TNN.2010.2040484

M. Siers and M. Z. Islam, Software defect prediction using a cost sensitive decision forest and voting, and a potential solution to the class imbalance problem, Information Systems, vol.51, pp.51-62, 2015.
DOI : 10.1016/j.is.2015.02.006

B. Sluban, D. Gamberger, and N. Lavrac, Ensemble-based noise detection: noise ranking and visual performance evaluation, Data Mining and Knowledge Discovery, vol.22, issue.2, pp.1-39, 2013.
DOI : 10.1007/s10462-004-0751-8

B. Sluban and N. Lavrac, Relating ensemble diversity and performance: A study in class noise detection, Neurocomputing, vol.160, pp.160-120, 2015.
DOI : 10.1016/j.neucom.2014.10.086

A. J. Smola and P. J. Bartlett, Advances in Large Margin Classifiers, 2000.

A. Stumpf and N. Kerle, Object-oriented mapping of landslides using Random Forests, Remote Sensing of Environment, vol.115, issue.10, pp.2564-2577, 2011.
DOI : 10.1016/j.rse.2011.05.013

A. Stumpf, N. Lachiche, J. P. Malet, N. Kerle, and A. Puissant, Active Learning in the Spatial Domain for Remote Sensing Image Classification, IEEE Transactions on Geoscience and Remote Sensing, vol.52, issue.5, pp.52-2492, 2014.
DOI : 10.1109/TGRS.2013.2262052

T. Sun, L. Jiao, J. Feng, F. Liu, and X. Zhang, Imbalanced Hyperspectral Image Classification Based on Maximum Margin, IEEE Geoscience and Remote Sensing Letters, vol.12, issue.3, pp.522-526, 2015.
DOI : 10.1109/LGRS.2014.2349272

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.40-3358, 2007.
DOI : 10.1016/j.patcog.2007.04.009

Z. Sun, Q. Song, X. Zhu, H. Sun, B. Xu et al., A novel ensemble method for classifying imbalanced data, Pattern Recognition, vol.48, issue.5, pp.48-1623, 2015.
DOI : 10.1016/j.patcog.2014.11.014

M. A. Tahir, J. Kittler, and F. Yan, Inverse random under sampling for class imbalance problem and its application to multi-label classification, Pattern Recognition, vol.45, issue.10, pp.45-3738, 2012.
DOI : 10.1016/j.patcog.2012.03.014

A. C. 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.

C. Teng, Correcting noisy data, Proceedings of the Sixteenth International Conference on Machine Learning, pp.239-248, 1999.

P. Thanathamathee and C. Lursinsap, Handling imbalanced data sets with synthetic boundary data generation using bootstrap re-sampling and AdaBoost techniques, Pattern Recognition Letters, vol.34, issue.12, pp.1339-1347, 2013.
DOI : 10.1016/j.patrec.2013.04.019

J. Thongkam, G. Xu, Y. Zhang, and F. Huang, Support Vector Machine for Outlier Detection in Breast Cancer Survivability Prediction, Lecture Notes in Computer Science, vol.4977, pp.99-109, 2008.
DOI : 10.1007/978-3-540-89376-9_10

T. Tran, D. Phung, and S. Venkatesh, Collaborative filtering via sparse Markov random fields, Information Sciences, vol.369, pp.221-237, 2016.
DOI : 10.1016/j.ins.2016.06.027

V. Vapnik, The Nature of Statistical Learning Theory, 1995.

S. Verbaeten and A. Assche, Ensemble Methods for Noise Elimination in Classification Problems, Multiple Classifier Systems, pp.317-325, 2003.
DOI : 10.1007/3-540-44938-8_32

A. Vezhnevets and O. Barinova, Avoiding Boosting Overfitting by Removing Confusing Samples, European Conference on Machine Learning: ECML 2007, pp.430-441, 2007.
DOI : 10.1007/978-3-540-74958-5_40

R. Y. Wang, V. C. Storey, and C. P. Firth, A framework for analysis of data quality research, IEEE Transactions on Knowledge and Data Engineering, vol.7, issue.4, pp.623-640, 1995.
DOI : 10.1109/69.404034

S. Wang and X. Yao, Diversity analysis on imbalanced data sets by using ensemble models, 2009 IEEE Symposium on Computational Intelligence and Data Mining, pp.324-331, 2009.
DOI : 10.1109/CIDM.2009.4938667

U. Wattanachon and C. Lursinsap, SPSM: A NEW HYBRID DATA CLUSTERING ALGORITHM FOR NONLINEAR DATA ANALYSIS, International Journal of Pattern Recognition and Artificial Intelligence, vol.17, issue.08, pp.1701-1737, 2009.
DOI : 10.1109/TNN.2005.845141

V. Wheway, Using Boosting to Detect Noisy Data, Advances in Artificial Intelligence . Pacific Rim International Conference on Artificial Intelligence, pp.123-130, 2000.
DOI : 10.1007/3-540-45408-X_13

T. Windeatt, Diversity measures for multiple classifier system analysis and design ., Information Fusion, pp.21-36, 2005.

Z. X. Xie, Y. Xu, Q. H. Hu, and P. F. Zhu, Margin distribution based bagging pruning, Neurocomputing, vol.85, pp.85-96, 2012.
DOI : 10.1016/j.neucom.2011.12.030

J. Zhang and I. Mani, Knn approach to unbalanced data distributions: A case study involving information extraction, Proceedings of the ICML'2003 Workshop on Learning from Imbalanced Datasets, 2003.

L. Zhang and P. N. Suganthan, Random Forests with ensemble of feature spaces, Pattern Recognition, vol.47, issue.10, pp.47-3429, 2014.
DOI : 10.1016/j.patcog.2014.04.001

Y. Zhang and W. N. Street, Bagging with Adaptive Costs, IEEE Transactions on Knowledge and Data Engineering, vol.20, issue.5, pp.577-588, 2008.
DOI : 10.1109/TKDE.2007.190724

Y. G. Zhang, B. L. Zhang, F. Coenenz, and W. J. Lu, Highly reliable breast cancer diagnosis with cascaded ensemble classifiers, The 2012 International Joint Conference on Neural Networks, pp.1-8, 2012.

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

Z. Zhou, Ensemble Methods: Foundations and Algorithms, 2012.

Z. H. Zhou and Y. Jiang, Medical diagnosis with c4.5 rule preceded by artificial neural network ensemble, IEEE Transactions on Information Technology in Biomedicine, vol.7, issue.1, pp.37-42, 2003.
DOI : 10.1109/TITB.2003.808498

X. Zhu and X. Wu, Class Noise vs. Attribute Noise: A Quantitative Study, Artificial Intelligence Review, vol.3, issue.4, pp.177-210, 2004.
DOI : 10.1080/07421222.1996.11518099

X. Q. Zhu, X. D. Wu, and Q. J. Chen, Eliminating class noise in large datasets, Proceeding of International Conference on Machine Learning, ICML2003, pp.920-927, 2003.