暂无分享,去创建一个
Sankha Subhra Mullick | Swagatam Das | Shounak Datta | Sayak Nag | Swagatam Das | S. S. Mullick | Shounak Datta | Sayak Nag
[1] Joarder Kamruzzaman,et al. z-SVM: An SVM for Improved Classification of Imbalanced Data , 2006, Australian Conference on Artificial Intelligence.
[2] A. Freitas,et al. Evaluating Six Candidate Solutions for the Small-Disjunct Problem and Choosing the Best Solution via Meta-Learning , 2005, Artificial Intelligence Review.
[3] Edward Y. Chang,et al. Adaptive Feature-Space Conformal Transformation for Imbalanced-Data Learning , 2003, ICML.
[4] José Salvador Sánchez,et al. Dissimilarity-Based Learning from Imbalanced Data with Small Disjuncts and Noise , 2015, IbPRIA.
[5] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[6] Lei Wang,et al. AdaBoost with SVM-based component classifiers , 2008, Eng. Appl. Artif. Intell..
[7] Yoshua Bengio,et al. Boosting Neural Networks , 2000, Neural Computation.
[8] Edward Y. Chang,et al. Aligning boundary in kernel space for learning imbalanced dataset , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).
[9] Si Wu,et al. Improving support vector machine classifiers by modifying kernel functions , 1999, Neural Networks.
[10] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Stan Matwin,et al. Addressing the Curse of Imbalanced Training Sets: One-Sided Selection , 1997, ICML.
[12] Alfredo Petrosino,et al. Adjusted F-measure and kernel scaling for imbalanced data learning , 2014, Inf. Sci..
[13] Gary M. Weiss. Mining with Rare Cases , 2010, Data Mining and Knowledge Discovery Handbook.
[14] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[15] Fernando Lozano,et al. Boosting of support vector machines with application to editing , 2005, Fourth International Conference on Machine Learning and Applications (ICMLA'05).
[16] Haym Hirsh,et al. A Quantitative Study of Small Disjuncts , 2000, AAAI/IAAI.
[17] F. Wilcoxon. Individual Comparisons by Ranking Methods , 1945 .
[18] R. Bristow,et al. Radiotherapy-induced miR-223 prevents relapse of breast cancer by targeting the EGF pathway , 2016, Oncogene.
[19] María José del Jesús,et al. KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining , 2017, Int. J. Comput. Intell. Syst..
[20] Francisco Herrera,et al. A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[21] Taghi M. Khoshgoftaar,et al. RUSBoost: A Hybrid Approach to Alleviating Class Imbalance , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.
[22] Yong Zhang,et al. Imbalanced data classification based on scaling kernel-based support vector machine , 2014, Neural Computing and Applications.
[23] Si Wu,et al. Conformal Transformation of Kernel Functions: A Data-Dependent Way to Improve Support Vector Machine Classifiers , 2002, Neural Processing Letters.
[24] Edward Y. Chang,et al. KBA: kernel boundary alignment considering imbalanced data distribution , 2005, IEEE Transactions on Knowledge and Data Engineering.
[25] David J. Hand,et al. A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems , 2001, Machine Learning.
[26] Alfredo Petrosino,et al. Asymmetric Kernel Scaling for Imbalanced Data Classification , 2011, WILF.
[27] M. Maloof. Learning When Data Sets are Imbalanced and When Costs are Unequal and Unknown , 2003 .
[28] Peter Williams,et al. Scaling the Kernel Function to Improve Performance of the Support Vector Machine , 2005, ISNN.
[29] Swagatam Das,et al. Near-Bayesian Support Vector Machines for imbalanced data classification with equal or unequal misclassification costs , 2015, Neural Networks.
[30] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[31] Haibin Ling,et al. Exclusivity Regularized Machine: A New Ensemble SVM Classifier , 2017, IJCAI.
[32] Daniel Boley,et al. On Approximate Solutions to Support Vector Machines , 2006, SDM.
[33] J. Ross Quinlan. Improved Estimates for the Accuracy of Small Disjuncts , 2005, Machine Learning.
[34] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[35] Haibo He,et al. Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.
[36] J. Koski. Multicriterion Optimization in Structural Design , 1981 .