An iterative method for multi-class cost-sensitive learning (2004)

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by Naoki Abe
Venue:In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Citations:24 - 0 self

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1 Cost-Sensitive Learning with Conditional Markov Networks – Prithviraj Sen, Lise Getoor
2 Benefit Maximizing Classification Using Feature Intervals – Nazli Ikizler, Cevdet Aykanat, Prof Dr, Prof Dr, Mehmet Baray - 2002
60 Editorial: Special Issue on Learning from Imbalanced Data Sets – Nitesh V. Chawla, Nathalie Japkowicz - 2004
17 Exploratory Under-Sampling for Class-Imbalance Learning – Xu-ying Liu, Jianxin Wu, Zhi-hua Zhou
2 Inducing cost-sensitive nonlinear decision trees – Sunil Vadera - 2005
5 Cost-Sensitive Boosting – Hamed Masnadi-shirazi, Nuno Vasconcelos, Hamed Masnadi-shirazi, Nuno Vasconcelos - 2007
Present address for corresponding author: – William Klement A, Szymon Wilk B, Wojtek Michalowski B, Ken J. Farion B, Martin H. Osmond D, Vedat Verter F, Dr. William Klement
Anytime learning of anycost classifiers – Saher Esmeir, Shaul Markovitch
35 Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers – Dragos Margineantu, Thomas G. Dietterich - 2000
7 Automatically countering imbalance and its empirical relationship to cost – Nitesh V. Chawla, David A. Cieslak, Lawrence O. Hall, Ajay Joshi - 2008
4 Discriminative Techniques for the Recognition of Complex-Shaped Objects – Owen Carmichael - 2003
1 Building Ensembles of Classifiers for Loss Minimization – Dragos D. Margineantu - 1999
5 Handling imbalanced datasets: A review – Sotiris Kotsiantis, Dimitris Kanellopoulos, Panayiotis Pintelas
Inducing Safer Oblique Trees without Costs – Sunil Vadera - 2005
3 The Effect Of Small Disjuncts And Class Distribution On Decision Tree Learning – Gary Mitchell Weiss, Gary Mitchell Weiss, Gary Mitchell Weiss, Dissertation Director, Haym Hirsh, Haym Hirsh - 2003
4 Roughly Balanced Bagging for Imbalanced Data – Shohei Hido, et al. - 2008
66 Cost-Sensitive Learning by Cost-Proportionate Example Weighting – Bianca Zadrozny, John Langford, Naoki Abe - 2003
19 Is random model better? on its accuracy and efficiency – Wei Fan, Haixun Wang, Philip S. Yu, Sheng Ma - 2003
77 Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers – Bianca Zadrozny, Charles Elkan - 2001