Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Synergy of clustering multiple back propagation networks
Advances in neural information processing systems 2
Evaluation of adaptive mixtures of competing experts
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Original Contribution: Stacked generalization
Neural Networks
Combining the results of several neural network classifiers
Neural Networks
Learning from a Population of Hypotheses
Machine Learning - Special issue on COLT '93
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Adaptive mixtures of local experts
Neural Computation
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Learning to Recognize Volcanoes on Venus
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Boosting classifiers regionally
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Combining email models for false positive reduction
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Naïve Bayes ensemble learning based on oracle selection
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Selective ensemble of decision trees
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Diagnostic powertracing for sensor node failure analysis
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
A class-specific ensemble feature selection approach for classification problems
Proceedings of the 48th Annual Southeast Regional Conference
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An ensemble is a classifier created by combining the predictions of multiple component classifiers. We present a new method for combining classifiers into an ensemble based on a simple estimation of each classifier's competence. The classifiers are grouped into an ordered list where each classifier has a corresponding threshold. To classify an example, the first classifier on the list is consulted and if that classifier's confidence for predicting the example is above the classifier's threshold, then that classifier's prediction is used. Otherwise, the next classifier and its threshold is consulted and so on. If none of the classifiers predicts the example above its confidence threshold then the class of the example is predicted by averaging all of the component classifier predictions. The key to this method is the selection of the confidence threshold for each classifier. We have implemented this method in a system called SEQUEL which has been applied to the task of recognizing volcanos in SAR images of Venus. In this domain, SEQUEL outperforms each individual classifier as well as the simple approach of using an ensemble constructed from the average prediction of all the classifiers.