Boosting with Lexicographic Programming: Addressing Class Imbalance without Cost Tuning
暂无分享,去创建一个
[1] Gunnar Rätsch,et al. Soft Margins for AdaBoost , 2001, Machine Learning.
[2] Gavin Brown,et al. Calibrating AdaBoost for Asymmetric Learning , 2015, MCS.
[3] Sang M. Lee,et al. Goal programming for decision analysis , 1972 .
[4] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[5] M. Maloof. Learning When Data Sets are Imbalanced and When Costs are Unequal and Unknown , 2003 .
[6] David J. Hand,et al. A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems , 2001, Machine Learning.
[7] Chen Huang,et al. Learning Deep Representation for Imbalanced Classification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Dale Schuurmans,et al. Boosting in the Limit: Maximizing the Margin of Learned Ensembles , 1998, AAAI/IAAI.
[9] Paul A. Viola,et al. Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade , 2001, NIPS.
[10] David Mease. Cost-Weighted Boosting with Jittering and Over / Under-Sampling : JOUS-Boost , 2004 .
[11] Xuebing Yang,et al. AMDO: An Over-Sampling Technique for Multi-Class Imbalanced Problems , 2018, IEEE Transactions on Knowledge and Data Engineering.
[12] Nuno Vasconcelos,et al. Cost-Sensitive Boosting , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[13] Yoav Freund,et al. Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.
[14] H. B. Mann,et al. On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .
[15] Fang Liu,et al. Imbalanced Hyperspectral Image Classification Based on Maximum Margin , 2015, IEEE Geoscience and Remote Sensing Letters.
[16] Lorenzo Bruzzone,et al. Classification of Hyperspectral Images With Regularized Linear Discriminant Analysis , 2009, IEEE Transactions on Geoscience and Remote Sensing.
[17] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[18] Nuno Vasconcelos,et al. Asymmetric boosting , 2007, ICML '07.
[19] 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.
[20] Kai Ming Ting,et al. A Comparative Study of Cost-Sensitive Boosting Algorithms , 2000, ICML.
[21] 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..
[22] Gaofeng Meng,et al. Spectral Unmixing via Data-Guided Sparsity , 2014, IEEE Transactions on Image Processing.
[23] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Stephen J. Wright,et al. Primal-Dual Interior-Point Methods , 1997 .
[25] Marco Cococcioni,et al. Lexicographic multi-objective linear programming using grossone methodology: Theory and algorithm , 2018, Appl. Math. Comput..
[26] Jure Leskovec,et al. Linear Programming Boosting for Uneven Datasets , 2003, ICML.
[27] Shigeru Katagiri,et al. Confusion-Matrix-Based Kernel Logistic Regression for Imbalanced Data Classification , 2017, IEEE Transactions on Knowledge and Data Engineering.
[28] Bartosz Krawczyk,et al. Learning from imbalanced data: open challenges and future directions , 2016, Progress in Artificial Intelligence.
[29] Vipin Kumar,et al. Evaluating boosting algorithms to classify rare classes: comparison and improvements , 2001, Proceedings 2001 IEEE International Conference on Data Mining.
[30] Gunnar Rätsch,et al. Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[31] Yang Wang,et al. Cost-sensitive boosting for classification of imbalanced data , 2007, Pattern Recognit..
[32] Damminda Alahakoon,et al. Minority report in fraud detection: classification of skewed data , 2004, SKDD.
[33] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[34] Zhi-Hua Zhou,et al. ON MULTI‐CLASS COST‐SENSITIVE LEARNING , 2006, Comput. Intell..
[35] Herna L. Viktor,et al. Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach , 2004, SKDD.
[36] R. Schapire. The Strength of Weak Learnability , 1990, Machine Learning.
[37] C. Romero. Extended lexicographic goal programming: a unifying approach , 2001 .
[38] N. Dopuch,et al. Management Goals and Accounting for Control. , 1967 .
[39] Jacek M. Zurada,et al. Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance , 2008, Neural Networks.
[40] M. Zarepisheh,et al. A dual-based algorithm for solving lexicographic multiple objective programs , 2007, Eur. J. Oper. Res..
[41] F. Wilcoxon. Individual Comparisons by Ranking Methods , 1945 .
[42] Chunhua Shen,et al. On the Dual Formulation of Boosting Algorithms , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[43] Xin Yao,et al. Multiclass Imbalance Problems: Analysis and Potential Solutions , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[44] M. Friedman. The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance , 1937 .
[45] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[46] Joelle Pineau,et al. Online Bagging and Boosting for Imbalanced Data Streams , 2013, IEEE Transactions on Knowledge and Data Engineering.
[47] Bartosz Krawczyk. Cost-sensitive one-vs-one ensemble for multi-class imbalanced data , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[48] Nitesh V. Chawla,et al. SMOTEBoost: Improving Prediction of the Minority Class in Boosting , 2003, PKDD.
[49] D. A. Conway. Management Goals and Accounting for Control , 1966 .
[50] Roger M. Y. Ho,et al. Goal programming and extensions , 1976 .
[51] Stan Matwin,et al. Addressing the Curse of Imbalanced Training Sets: One-Sided Selection , 1997, ICML.
[52] Bidyut Baran Chaudhuri,et al. Handling data irregularities in classification: Foundations, trends, and future challenges , 2018, Pattern Recognit..
[53] Peter A. Flach,et al. Cost-sensitive boosting algorithms: Do we really need them? , 2016, Machine Learning.
[54] Haibo He,et al. RAMOBoost: Ranked Minority Oversampling in Boosting , 2010, IEEE Transactions on Neural Networks.
[55] J. K. Sankaran,et al. On a variant of lexicographic multi-objective programming , 1998, Eur. J. Oper. Res..
[56] Fernando Charro,et al. A mixed problem for the infinity Laplacian via Tug-of-War games , 2007, 0706.4267.
[57] José Luis Alba-Castro,et al. Double-base asymmetric AdaBoost , 2013, Neurocomputing.
[58] 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).
[59] W. Kruskal,et al. Use of Ranks in One-Criterion Variance Analysis , 1952 .
[60] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.
[61] Y. Peres,et al. Tug-of-war and the infinity Laplacian , 2006, math/0605002.