Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining and Knowledge Discovery
Class Probability Estimation and Cost-Sensitive Classification Decisions
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Automatically countering imbalance and its empirical relationship to cost
Data Mining and Knowledge Discovery
A Simple Lexicographic Ranker and Probability Estimator
ECML '07 Proceedings of the 18th European conference on Machine Learning
Cost-Based Sampling of Individual Instances
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
IEEE Transactions on Knowledge and Data Engineering
Integrating selective pre-processing of imbalanced data with Ivotes ensemble
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Severe class imbalance: why better algorithms aren't the answer
ECML'05 Proceedings of the 16th European conference on Machine Learning
Artificial Intelligence in Medicine
Foundation of mining class-imbalanced data
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Automatic and continuous user task analysis via eye activity
Proceedings of the 2013 international conference on Intelligent user interfaces
IIvotes ensemble for imbalanced data
Intelligent Data Analysis - Combined Learning Methods and Mining Complex Data
| Hi-index | 0.00 |
Learning from data with severe class imbalance is difficult. Established solutions include: under-sampling, adjusting classification threshold, and using an ensemble. We examine the performance of combining these solutions to balance the sensitivity and specificity for binary classifications, and to reduce the MSE score for probability estimation.