Information-Based Evaluation Criterion for Classifier's Performance
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Characterizing the applicability of classification algorithms using meta-level learning
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
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ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Combining Multiple Models with Meta Decision Trees
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Issues in stacked generalization
Journal of Artificial Intelligence Research
Stacking with an Extended Set of Meta-level Attributes and MLR
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Reliable Classifications with Machine Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Stacking with Multi-response Model Trees
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Transductive machine learning for reliable medical diagnostics
Journal of Medical Systems - Special issue: Computer-based medical systems
Quality assessment of individual classifications in machine learning and data mining
Knowledge and Information Systems
Intelligent Data Analysis
Credit risk analysis using a hybrid data mining model
International Journal of Intelligent Systems Technologies and Applications
Methods and algorithms of collective recognition
Automation and Remote Control
Diversity-Based Classifier Selection for Adaptive Object Tracking
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Troika - An improved stacking schema for classification tasks
Information Sciences: an International Journal
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IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Human-machine interaction issues in quality control based on online image classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Bagging different instead of similar models for regression and classification problems
International Journal of Computer Applications in Technology
GA-stacking: Evolutionary stacked generalization
Intelligent Data Analysis
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Learning visual quality inspection from multiple humans using ensembles of classifiers
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Reranking for stacking ensemble learning
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
A multilevel information fusion approach for visual quality inspection
Information Fusion
Artificial recurrence for classification of streaming data with concept shift
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
A systematic discussion of fusion techniques for multi-modal affect recognition tasks
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
Learning about the learning process
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Error-sensitive grading for model combination
ECML'05 Proceedings of the 16th European conference on Machine Learning
Transductive reliability estimation for medical diagnosis
Artificial Intelligence in Medicine
Credit rating using a hybrid voting ensemble
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Detection of CAN by ensemble classifiers based on ripple down rules
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
A multi-tier ensemble construction of classifiers for phishing email detection and filtering
CSS'12 Proceedings of the 4th international conference on Cyberspace Safety and Security
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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In this paper, we discuss grading, a meta-classification technique that tries to identify and correct incorrect predictions at the base level. While stacking uses the predictions of the base classifiers as meta-level attributes, we use "graded" predictions (i.e., predictions that have been marked as correct or incorrect) as meta-level classes. For each base classifier, one meta classifier is learned whose task is to predict when the base classifier will err. Hence, just like stacking may be viewed as a generalization of voting, grading may be viewed as a generalization of selection by cross-validation and therefore fills a conceptual gap in the space of meta-classification schemes. Our experimental evaluation shows that this technique results in a performance gain that is quite comparable to that achieved by stacking, while both, grading and stacking outperform their simpler counter-parts voting and selection by cross-validation.