Robust classification systems for imprecise environments
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Robust Classification for Imprecise Environments
Machine Learning
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Using Rule Sets to Maximize ROC Performance
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Predicting good probabilities with supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
ROCCER: an algorithm for rule learning based on ROC analysis
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
AUC: a better measure than accuracy in comparing learning algorithms
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Learning trees and rules with set-valued features
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Guest editorial: special issue on utility-based data mining
Data Mining and Knowledge Discovery
Facetwise analysis of XCS for problems with class imbalances
IEEE Transactions on Evolutionary Computation
Induction and pruning of classification rules for prediction of microseismic hazards in coal mines
Expert Systems with Applications: An International Journal
Performance of classification models from a user perspective
Decision Support Systems
Rule quality measure-based induction of unordered sets of regression rules
AIMSA'12 Proceedings of the 15th international conference on Artificial Intelligence: methodology, systems, and applications
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Rules are commonly used for classification because they are modular, intelligible and easy to learn. Existing work in classification rule learning assumes the goal is to produce categorical classifications to maximize classification accuracy. Recent work in machine learning has pointed out the limitations of classification accuracy: when class distributions are skewed, or error costs are unequal, an accuracy maximizing classifier can perform poorly. This paper presents a method for learning rules directly from ROC space when the goal is to maximize the area under the ROC curve (AUC). Basic principles from rule learning and computational geometry are used to focus the search for promising rule combinations. The result is a system that can learn intelligible rulelists with good ROC performance.