Original Contribution: Stacked generalization
Neural Networks
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
Robust Classification for Imprecise Environments
Machine Learning
Arbitrating among competing classifiers using learned referees
Knowledge and Information Systems
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An Evaluation of Grading Classifiers
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Tree Induction for Probability-Based Ranking
Machine Learning
Ensembles of biased classifiers
ICML '05 Proceedings of the 22nd international conference on Machine learning
Optimizing abstaining classifiers using ROC analysis
ICML '05 Proceedings of the 22nd international conference on Machine learning
Multi-level Classification of Emphysema in HRCT Lung Images Using Delegated Classifiers
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
ROCCER: an algorithm for rule learning based on ROC analysis
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Decision forests with oblique decision trees
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
ML-CIDIM: multiple layers of multiple classifier systems based on CIDIM
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
From local to global patterns: evaluation issues in rule learning algorithms
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
E-CIDIM: ensemble of CIDIM classifiers
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Supervised learning with minimal effort
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Journal of Computer and System Sciences
An introduction to artificial prediction markets for classification
The Journal of Machine Learning Research
On the effect of calibration in classifier combination
Applied Intelligence
The data replication method for the classification with reject option
AI Communications
Automatic selection of classification learning algorithms for data mining practitioners
Intelligent Data Analysis
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A sensible use of classifiers must be based on the estimated reliability of their predictions. A cautious classifier would delegate the difficult or uncertain predictions to other, possibly more specialised, classifiers. In this paper we analyse and develop this idea of delegating classifiers in a systematic way. First, we design a two-step scenario where a first classifier chooses which examples to classify and delegates the difficult examples to train a second classifier. Secondly, we present an iterated scenario involving an arbitrary number of chained classifiers. We compare these scenarios to classical ensemble methods, such as bagging and boosting. We show experimentally that our approach is not far behind these methods in terms of accuracy, but with several advantages: (i) improved efficiency, since each classifier learns from fewer examples than the previous one; (ii) improved comprehensibility, since each classification derives from a single classifier; and (iii) the possibility to simplify the overall multi-classifier by removing the parts that lead to delegation.