Imbalanced dataset classification and solutions: a review, International Journal of Computing and Business Research (IJCBR), vol.5, issue.4, 2014. ,
A method for resampling imbalanced datasets in binary classification tasks for real-world problems, Neurocomputing, vol.135, pp.32-41, 2014. ,
Solving the multiple instance problem with axis-parallel rectangles, Artifical Intelligence, vol.89, issue.1, pp.31-71, 1997. ,
On learning from multi-instance examples: empirical evaluation of a theoretical approach, Proceedings of the International Conference on Machine Learning (ICML), pp.21-29, 1997. ,
A framework for multiple-instance learning, Advances in Neural Information Processing Systems (NIPS), pp.570-576, 1998. ,
EM-DD: an improved multi-instance learning technique, Advances in Neural Information Processing Systems (NIPS), pp.1073-1080, 2002. ,
Solving the multiple-instance problem: a lazy learning approach, Proceedings of the International Conference on Machine Learning (ICML), pp.1119-1125, 2000. ,
Support vector machines for multiple-instance learning, Advances in Neural Information Processing Systems (NIPS), pp.561-568, 2003. ,
Multi-instance kernels, Proceedings of the International Conference on Machine Learning (ICML), pp.179-186, 2002. ,
Image categorization by learning and reasoning with regions, J. of Machine Learning Res, vol.5, pp.913-939, 2004. ,
Miles: Multiple-instance learning via embedded instance selection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.12, pp.1931-1947, 2006. ,
A regularization framework for multipleinstance learning, Proceedings of the International Conference on Machine Learning (ICML), 2006. ,
On the relation between multi-instance learning and semi-supervised learning, Proceedings of the International Conference on Machine Learning (ICML), 2007. ,
Multi-instance learning by treating instances as non-iid samples, Proceedings of the International Conference on Machine Learning, pp.1249-1256, 2009. ,
Scalable algorithms for multiinstance learning, IEEE Transactions on Neural Networks and Learning Systems, vol.28, issue.4, pp.975-987, 2017. ,
Learning single and multiple instance decision trees for computer security applications, 2000. ,
Solving multiple-instance and multiplepart learning problems with decision trees and decision rules. application to the mutagenesis problem, Proceedings of the 14th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, pp.204-214, 2001. ,
URL : https://hal.archives-ouvertes.fr/hal-02545010
Multi-instance tree learning, Proceedings of the International Conference on Machine Learning (ICML), pp.57-64, 2005. ,
MIForests: multiple-instance learning with randomized trees, Proc. of the International Conference on Computer Vision and Pattern Recognition (CVPR), pp.29-42, 2010. ,
Multiple instance learning with response-optimized random forests, Proc. of the International Conference on International Conference on Pattern Recognition (ICPR), 2014. ,
A two-level learning method for generalized multi-instance problem, Proceedings of the European Conference on Machine Learning (ECML), pp.468-479, 2003. ,
Multiple instance learning with bag-level randomized trees, Proceedings of the European Conference on Machine Learning (ECML), 2018. ,
Extremely randomized trees, Machine Learning, vol.63, pp.3-42, 2006. ,
URL : https://hal.archives-ouvertes.fr/hal-00341932
Multi instance neural networks, Proceedings of the ICML 2000 workshop on attribute-value and relational learning, pp.53-60, 2000. ,
Neural networks for multi-instance learning, Tech. Rep, 2002. ,
Improve multi-instance neural networks through feature selection, Neural Processing Letters, vol.19, issue.1, pp.1-10, 2004. ,
, Adapting RBF neural networks to multi-instance learning, Neural Processing Letters, vol.23, issue.1, pp.1-26, 2006.
Revisiting multiple instance neural networks, Pattern Recognition, vol.74, pp.15-24, 2018. ,
Attention-based deep multiple instance learning, Proceedings of the International Conference on Machine Learning (ICML), pp.2127-2136, 2018. ,
Multiple instance learning with graph neural networks, Proceedings of the ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Data, 2019. ,
Making risk minimization tolerant to label noise, Neurocomputing, vol.160, pp.93-107, 2015. ,
Robust loss functions under label noise for deep neural networks, Proceedings of the AAAI Conference on Artificial Intelligence, pp.1919-1925, 2017. ,
Generalized cross entropy loss for training deep neural networks with noisy labels, Advances in Neural Information Processing Systems (NIPS), 2018. ,
An analysis of transformations, Journal of the Royal Statistical Society. Series B, pp.211-252, 1964. ,
Making deep neural networks robust to label noise: A loss correction approach, Proc. of the International Conference on Computer Vision and Pattern Recognition, pp.2233-2241, 2017. ,
Training deep neural networks on noisy labels with bootstrapping, 3rd International Conference on Learning Representations (ICLR), 2015. ,
Learning to reweight examples for robust deep learning, Proceedings of the International Conference on Machine Learning (ICML), pp.4331-4340, 2018. ,