PAKDD 2009 Workshop


PAKDD'2009 Workshop on

Data Mining When Classes are Imbalanced and Errors Have Costs

(A follow-up to AAAI'2000 Workshop on Learning from Imbalanced Data Sets and ICML'2003 Workshop on Learning from Imbalanced Data Sets II)



Date and Location:

Monday April 27, 2009 in Bangkok, Thailand Workshop Schedule

8:00-8:35: OPENING REMARKS, Nitesh Chawla and Zhi-Hua Zhou

8:35-9:25: KEYNOTE TALK, Charles Ling

9:25-10:10: Session 1: Theoretical and Empircal Studies

  • 9:25-9:40: Cheng G. Weng and Josiah Poon, Data Complexity Analysis for Imbalanced Datasets

  • 9:40-9:55: William Klement, Szymon Wilk, Wojtek Michaowski, and Stan Matwin Dealing with Severely Imbalanced Data

  • 9:55-10:10: Xu-Ying Liu and Zhi-Hua Zhou, Learning with Cost Intervals

10:10-10:30: Coffee Break

10:30-11:30: Session 2: Applications in Imbalance

  • 10:30-10:45: Kuo-Wei Hsu and Jaideep Srivastava, An Empirical Study of Applying Ensembles of Heterogeneous Classifiers on Imperfect Data

  • 10:45-11:00: En-hui Zheng, Chao Zou, Jian Sun, and Le Chen, SVM based Credit Card Fraud Detection with Reject Cost and Class-dependent Error Cost

  • 11:00-11:15: Sareewan Dendamrongvit and Miroslav Kubat Imbalanced Training Sets and Induction from Multi-Label Text-Categorization Domains

  • 11:15-11:30: Edisanter Lo Outlier Detection in Hyperspectral Imaging Based on Conditional Subspace Models

11:30-12:00: Session 3: Mining Streams of Imbalanced Data

  • 11:30-11:45: Ryan Lichtenwalter and Nitesh Chawla, Adaptive Methods for Classification in Arbitrarily Imbalanced and Drifting Data Streams

  • 11:30-12:00: Yi Wang, Yang Zhang, and Yong Wang, Classifier Ensemble for Mining Data Streams with Skewed Distribution

12:00-12:30: DISCUSSION AND CONCLUSION, Nitesh Chawla and Zhi-Hua Zhou