The weighted majority algorithm
Information and Computation
Learning in the presence of concept drift and hidden contexts
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
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An ensemble method for incremental classification in stationary and non-stationary environments
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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We work with a recently proposed algorithm where an ensemble of base classifiers, combined using weighted majority voting, is used for incremental classification of data. To successfully accommodate novel information without compromising previously acquired knowledge this algorithm requires an adequate strategy to determine the voting weights. Given an instance to classify, we propose to define each voting weight as the posterior probability of the corresponding hypothesis given the instance. By operating with priors and the likelihood models the obtained weights can take into account the location of the instance in the different class-specific feature spaces but also the coverage of each class k given the classifier and the quality of the learned hypothesis. This approach can provide important improvements in the generalization performance of the resulting classifier and its ability to control the stability/plasticity tradeoff. Experiments are carried out with three real classification problems already introduced to test incremental algorithms.