Training cost-sensitive neural networks with methods addressing the class imbalance problem

10.1023/A:1022859003006

10.1016/B978-1-55860-335-6.50034-9

margineantu, 2000, Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers, Proc 17th Int'l Conf Machine Learning, 583

maloof, 2003, Learning When Data Sets are Imbalanced and When Costs Are Unequal and Unknown, Proc Working Notes ICML'03 Workshop Learning from Imbalanced Data Sets

lawrence, 1998, Neural Network Classification and Prior Class Probabilities, Lecture Notes in Computer Science 1524, 299, 10.1007/3-540-49430-8_15

10.1109/21.52545

rumelhart, 1986, Learning Internal Representations by Error Propagation, Parallel Distributed Processing Explorations in the Microstructure of Cognition, 1, 318

dasarathy, 1991, Nearest Neighbor Norms NN Pattern Classification Techniques

provost, 2000, Machine Learning from Imbalanced Data Sets 101, Working Notes AAAI'00 Workshop Learning from Imbalanced Data Sets, 1

provost, 1997, Analysis and Visualization of Classifier Performance: Comparison Under Imprecise Class and Cost Distributions, Proc ACM SIGKDD Int Conf Knowledge Discovery and Data Mining, 43

brefeld, 2003, Support Vector Machines with Example Dependent Costs, Proc 14th European Conf Machine Learning, 23

bradford, 1998, Pruning Decision Trees with Misclassification Costs, Proc 10th European Conf Machine Learning, 131

2000, Machine Learning?A Technological Roadmap

chawla, 2002, SMOTE: Synthetic Minority Over-Sampling Technique, J Artificial Intelligence Research, 16, 321, 10.1613/jair.953

breiman, 1984, Classification and Regression Trees

10.1023/A:1007515423169

10.1145/1014052.1014056

blake, 1998, UCI Repository of Machine Learning Databases

10.1145/380995.381030

dietterich, 2002, Ensemble Learning, The Handbook of Brain Theory and Neural Networks

10.1145/312129.312220

10.1145/347090.347126

drummond, 2003, C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling Beats Over-Sampling, Working Notes of the ICML'03 Workshop Learning from Imbalanced Data Sets

ting, 2000, A Comparative Study of Cost-Sensitive Boosting Algorithms, Proc 17th Int'l Conf Machine Learning, 983

10.1145/7902.7906

ting, 2002, An Instance-Weighting Method to Induce Cost-Sensitive Trees, IEEE Trans Knowledge and Data Eng, 659, 10.1109/TKDE.2002.1000348

ting, 2000, An Empirical Study of MetaCost Using Boosting Algorithm, Proc 11th European Conf Machine Learning, 413

turney, 1995, Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm, J Artificial Intelligence Research, 2, 369, 10.1613/jair.120

tomek, 1976, Two Modifications of CNN, IEEE Trans Systems Man and Cybernetics, 6, 769, 10.1109/TSMC.1976.4309452

10.1145/775047.775143

10.1007/3-540-61532-6_3

10.1145/1007730.1007734

10.1145/502512.502540

kukar, 1998, Cost-Sensitive Learning with Neural Networks, Proc 13th European Conf Artificial Intelligence, 445

kubat, 1997, Addressing the Curse of Imbalanced Training Sets: One-Sided Selection, Proc 14th Int'l Conf Machine Learning, 179

japkowicz, 2000, Learning from Imbalanced Data Sets: A Comparison of Various Strategies, Working Notes of the AAAI'00 Workshop Learning from Imbalanced Data Sets, 10

elkan, 2001, The Foundations of Cost-Sensitive Learning, Proc 17th Int'l Joint Conf Artificial Intelligence, 973

knoll, 1994, Cost-Sensitive Pruning of Decision Trees, Proc Eighth European Conf Machine Learning, 383

japkowicz, 2002, The Class Imbalance Problem: A Systematic Study, Intelligent Data Analysis, 6, 429, 10.3233/IDA-2002-6504