Effective data generation for imbalanced learning using conditional generative adversarial networks
Tài liệu tham khảo
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C. et al. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from tensorflow.org.
Akbani, 2004, Applying support vector machine to imbalanced datasets, Machine Learning: ECML, 2004, 39
Barua, 2014, MWMOTE - majority weighted minority oversampling technique for imbalanced data set learning, IEEE Transactions on Knowledge and Data Engineering, 26, 405, 10.1109/TKDE.2012.232
Batista, 2004, A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data, ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets, 6, 20, 10.1145/1007730.1007735
Bunkhumpornpat, 2009, Safe-Level-SMOTE: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem, 475
Bunkhumpornpat, 2012, DBSMOTE: Density-based synthetic minority over-sampling technique, Applied Intelligence, 36, 664, 10.1007/s10489-011-0287-y
Chawla, 2002, SMOTE: Synthetic minority over-sampling technique, Journal of Artificial Intelligence Research, 16, 321, 10.1613/jair.953
Chawla, 2003, Workshop learning from imbalanced data sets II
Chawla, 2005, Data mining for imbalanced data sets: an overview, 875
Chawla, 2003, SMOTEBoost: Improving prediction of the minority class in boosting, 107
Cieslak, 2008, Start globally, optimize locally, predict globally: Improving performance on imbalanced data, 143
Clearwater, 1991, A rule-learning program in high energy physics event classification, Computer Physics Communications, 67, 159, 10.1016/0010-4655(91)90014-C
Cieslak, 2006, Combating imbalance in network intrusion datasets, 732
Cover, 1967, Nearest neighbour pattern classification, IEEE Transactions on Information Theory, 13, 21, 10.1109/TIT.1967.1053964
Domingos, 1999, MetaCost : A general method for making classifiers, 155
Douzas, 2017, Self-organizing map oversampling (SOMO) for imbalanced data set learning, Expert Systems with Applications, 82, 40, 10.1016/j.eswa.2017.03.073
Fernández, 2013, Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches, Knowledge-Based Systems, 42, 97, 10.1016/j.knosys.2013.01.018
Friedman, 2001, Greedy function approximation: A gradient boosting machine, Annals of Statistics, 29, 1189, 10.1214/aos/1013203451
Galar, 2012, 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, 42, 463, 10.1109/TSMCC.2011.2161285
Gauthier, 2015
Chang, 2011, LIBSVM: A library for support vector machines, ACM Transactions on Intelligent Systems and Technology, 2, 27, 10.1145/1961189.1961199
Glorot, 2011, Deep sparse rectifier neural networks, 15, 315
Goodfellow, 2014, Generative adversarial nets, Advances in Neural Information Processing Systems, 27, 2672
Graves, 2016, Tree species abundance predictions in a tropical agricultural landscape with a supervised classification model and imbalanced data, Remote Sensing, 8, 161, 10.3390/rs8020161
Guo, 2004, Learning from imbalanced data sets with boosting and data generation: The DataBoost IM approach, ACM SIGKDD Explorations Newsletter, 6, 30, 10.1145/1007730.1007736
Guyon, I. (2003). Design of experiments for the NIPS 2003 variable selection benchmark.
Han, 2005, Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning, Advances In Intelligent Computing, 17, 878, 10.1007/11538059_91
He, 2008, ADASYN: Adaptive synthetic sampling approach for imbalanced learning, 1322
He, 2009, Learning from imbalanced data, IEEE Transactions on Knowledge and Data Engineering, 21, 1263, 10.1109/TKDE.2008.239
Jo, 2004, Class imbalances versus small disjuncts, ACM Sigkdd Explorations Newsletter, 6, 40, 10.1145/1007730.1007737
Kingma, 2015, Adam: A method for stochastic optimization
Lemaitre, G., Nogueira, F., & Aridas, C. (2016). Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning. CoRR abs/1609.06570.
McCullagh, 1984, Generalized linear models, European Journal of Operational Reasearch, 16, 285, 10.1016/0377-2217(84)90282-0
Mehrjou, 2017, Annealed generative adversarial networks
Mirza, 2014, Conditional generative adversarial nets
Nekooeimehr, 2016, Adaptive semi-unsupervised weighted oversampling (A-SUWO) for imbalanced datasets, Expert Systems with Applications, 46, 405, 10.1016/j.eswa.2015.10.031
Pedregosa, 2011, Scikit-learn: Machine learning in python, Journal of Machine Learning Research, 12, 2825
Quinlan, 1993, 16, 235
Sun, 2015, A novel ensemble method for classifying imbalanced data, Pattern Recognition, 48, 1623, 10.1016/j.patcog.2014.11.014
Tang, 2015, KernelADASYN: Kernel based adaptive synthetic data generation for imbalanced learning, IEEE Congress on Evolutionary Computation (CEC)
Ting, 2002, An instance-weighting method to induce cost-sensitive trees, IEEE Transactions on Knowledge and Data Engineering, 14, 659, 10.1109/TKDE.2002.1000348
Verbeke, 2012, New insights into churn prediction in the telecommunication sector: A profit driven data mining approach, European Journal of Operational Research, 218, 211, 10.1016/j.ejor.2011.09.031
Wang, 2015, Resampling-based ensemble methods for online class imbalance learning, IEEE Transactions on Knowledge and Data Engineering, 27, 1356, 10.1109/TKDE.2014.2345380
Wilson, 1972, Asymptotic properties of nearest neighbor rules using edited data, IEEE Transactions on Systems, Man and Cybernetics, 2, 408, 10.1109/TSMC.1972.4309137
Zhao, 2008, Protein classification with imbalanced data, Proteins: Structure, Function and Genetics, 70, 1125, 10.1002/prot.21870