The Strength of Weak Learnability
Machine Learning
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Experimental comparisons of online and batch versions of bagging and boosting
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Online Ensemble Learning: An Empirical Study
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An Incremental Learning Method for Face Recognition under Continuous Video Stream
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Incremental rule learning based on example nearness from numerical data streams
Proceedings of the 2005 ACM symposium on Applied computing
2005 Special issue: Incremental learning of feature space and classifier for face recognition
Neural Networks - 2005 Special issue: IJCNN 2005
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
New ensemble methods for evolving data streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Issues in evaluation of stream learning algorithms
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Evolving connectionist systems for knowledge discovery from gene expression data of cancer tissue
Artificial Intelligence in Medicine
Incremental learning methods with retrieving of interfered patterns
IEEE Transactions on Neural Networks
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In this paper, we propose an incremental learning algorithm for ensemble classifier systems. Ensemble learning algorithms combine the predictions of multiple base models, each of which is learned using a traditional algorithm. We propose a new method to update weights of classifiers in the weighted majority voting scheme under the one-pass incremental learning situations. This method computes the weights of classifiers and the distribution of training data following an approach based on the computing of prequential error that avoids the overflow of internal values used by the learning algorithm. Using a prequential approach implies that learned samples are forgotten progressively. Forgetting learned concepts could influence the accuracy of the model. However, in the experiments, we verify that the proposed model can learn incrementally without serious forgetting and that the performance is not seriously influenced by the used reweighting method in comparison with learning models without forgetting. Experimental results confirm that the proposed incremental ensemble classifier system yields comparable performance with another learning ensemble classifier system. Moreover, it can be trained with open-ended data streams without data overflow.