Error reduction through learning multiple descriptions
Machine Learning
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A classification-based methodology for planning audit strategies in fraud detection
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Temporal sequence learning and data reduction for anomaly detection
ACM Transactions on Information and System Security (TISSEC)
Explicitly representing expected cost: an alternative to ROC representation
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Extracting Context-Sensitive Models in Inductive Logic Programming
Machine Learning
Using Data Mining Techniques in Fiscal Fraud Detection
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
An Assessment of ILP-Assisted Models for Toxicology and the PTE-3 Experiment
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
An Automated ILP Server in the Field of Bioinformatics
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Multiple Classifier Systems Based on Interpretable Linear Classifiers
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Evaluating classifiers' performance in a constrained environment
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Complexity in the case against accuracy estimation
Theoretical Computer Science
An intelligent system for customer targeting: a data mining approach
Decision Support Systems
Cost-sensitive classifier evaluation
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Optimizing abstaining classifiers using ROC analysis
ICML '05 Proceedings of the 22nd international conference on Machine learning
Propositionalization-based relational subgroup discovery with RSD
Machine Learning
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
ROC graphs with instance-varying costs
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Multi-class ROC analysis from a multi-objective optimisation perspective
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
A description of competing fusion systems
Information Fusion
Empirical analysis of the evolution of a taxonomy for best practices
Decision Support Systems
Classification of intrusion detection alerts using abstaining classifiers
Intelligent Data Analysis
On reoptimizing multi-class classifiers
Machine Learning
PRIE: a system for generating rulelists to maximize ROC performance
Data Mining and Knowledge Discovery
Classifying Chemical Compounds Using Contrast and Common Patterns
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
A Sensitivity Clustering Method for Hybrid Evolutionary Algorithms
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part I: Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira's Scientific Legacy
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
An empirical evaluation of bagging in inductive logic programming
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks
IEEE Transactions on Neural Networks
An intervention mechanism for assistive living in smart homes
Journal of Ambient Intelligence and Smart Environments
Linguistic cost-sensitive learning of genetic fuzzy classifiers for imprecise data
International Journal of Approximate Reasoning
Severe class imbalance: why better algorithms aren't the answer
ECML'05 Proceedings of the 16th European conference on Machine Learning
Local patterns: theory and practice of constraint-based relational subgroup discovery
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
A two-stage evolutionary algorithm based on sensitivity and accuracy for multi-class problems
Information Sciences: an International Journal
Job performance prediction in a call center using a naive Bayes classifier
Expert Systems with Applications: An International Journal
A multi-objective neural network based method for cover crop identification from remote sensed data
Expert Systems with Applications: An International Journal
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In real-world environments it is usually difficult to specify target operating conditions precisely. This uncertainty makes building robust classification systems problematic. We show that it is possible to build a hybrid classifier that will perform at least as well as the best available classifier for any target conditions. This robust performance extends across a wide variety of comparison frameworks, including the optimization of metrics such as accuracy, expected cost, lift, precision, recall, and workforce utilization. In some cases, the performance of the hybrid can actually surpass that of the best known classifier. The hybrid is also efficient to build, to store, and to update. Finally, we provide empirical evidence that a robust hybrid classifier is needed for many real-world problems.