Example-dependent cost-sensitive adaptive boosting
暂无分享,去创建一个
[1] Salvatore J. Stolfo,et al. AdaCost: Misclassification Cost-Sensitive Boosting , 1999, ICML.
[2] Ulf Brefeld,et al. Support Vector Machines with Example Dependent Costs , 2003, ECML.
[3] Yang Wang,et al. Cost-sensitive boosting for classification of imbalanced data , 2007, Pattern Recognit..
[4] Kai Ming Ting,et al. An Instance-weighting Method to Induce Cost-sensitive Trees , 2001 .
[5] José Luis Alba-Castro,et al. Shedding light on the asymmetric learning capability of AdaBoost , 2012, Pattern Recognit. Lett..
[6] Gavin Brown,et al. Calibrating AdaBoost for Asymmetric Learning , 2015, MCS.
[7] Zhi-Hua Zhou,et al. The Influence of Class Imbalance on Cost-Sensitive Learning: An Empirical Study , 2006, Sixth International Conference on Data Mining (ICDM'06).
[8] Theoden Netoff,et al. Seizure prediction with spectral power of EEG using cost‐sensitive support vector machines , 2011, Epilepsia.
[9] Yufei Xia,et al. Cost-sensitive boosted tree for loan evaluation in peer-to-peer lending , 2017, Electron. Commer. Res. Appl..
[10] Peter A. Flach,et al. Cost-sensitive boosting algorithms: Do we really need them? , 2016, Machine Learning.
[11] Bianca Zadrozny,et al. Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers , 2001, ICML.
[12] Zhi-Hua Zhou,et al. Ieee Transactions on Knowledge and Data Engineering 1 Training Cost-sensitive Neural Networks with Methods Addressing the Class Imbalance Problem , 2022 .
[13] Gerald Schaefer,et al. Cost-sensitive decision tree ensembles for effective imbalanced classification , 2014, Appl. Soft Comput..
[14] Ekrem Duman,et al. A cost-sensitive decision tree approach for fraud detection , 2013, Expert Syst. Appl..
[15] Francisco Javier García Castellano,et al. Expert Systems With Applications , 2022 .
[16] Kai Ming Ting,et al. A Comparative Study of Cost-Sensitive Boosting Algorithms , 2000, ICML.
[17] Björn E. Ottersten,et al. Improving Credit Card Fraud Detection with Calibrated Probabilities , 2014, SDM.
[18] Zdzislaw Piasta,et al. Rough Classifiers Sensitive to Costs Varying from Object to Object , 1998, Rough Sets and Current Trends in Computing.
[19] Gabriel J. Brostow,et al. Revisiting Example Dependent Cost-Sensitive Learning with Decision Trees , 2013, 2013 IEEE International Conference on Computer Vision.
[20] Björn E. Ottersten,et al. Example-Dependent Cost-Sensitive Logistic Regression for Credit Scoring , 2014, 2014 13th International Conference on Machine Learning and Applications.
[21] Anazida Zainal,et al. Fraud detection system: A survey , 2016, J. Netw. Comput. Appl..
[22] Paul A. Viola,et al. Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade , 2001, NIPS.
[23] Bianca Zadrozny,et al. Learning and making decisions when costs and probabilities are both unknown , 2001, KDD '01.
[24] Peter A. Flach,et al. A Unified View of Performance Metrics: Translating Threshold Choice into Expected Classification Loss C` Esar Ferri , 2012 .
[25] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[26] Haibo He,et al. Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.
[27] Trevor Hastie,et al. The Elements of Statistical Learning , 2001 .
[28] Rich Caruana,et al. Obtaining Calibrated Probabilities from Boosting , 2005, UAI.
[29] Nuno Vasconcelos,et al. Asymmetric boosting , 2007, ICML '07.
[30] Charles Elkan,et al. The Foundations of Cost-Sensitive Learning , 2001, IJCAI.
[31] Björn E. Ottersten,et al. Example-dependent cost-sensitive decision trees , 2015, Expert Syst. Appl..
[32] John Langford,et al. Cost-sensitive learning by cost-proportionate example weighting , 2003, Third IEEE International Conference on Data Mining.
[33] John Platt,et al. Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .
[34] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[35] J. Friedman. Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .