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Observational learning algorithm for heterogeneous ensembles. (English) Zbl 1123.68356

Summary: The ensemble method has shown the potential to increase classification accuracy beyond the level reached by an individual classifier alone. Observational Learning Algorithm (OLA) is an ensemble method based on social learning theory. Previous work mainly focused on OLA for homogeneous ensembles, such as neural networks ensembles. In this paper, OLA for heterogeneous ensembles, which is a process with three steps: training, observing, and retraining is proposed. Experiments on five datasets from the UCI repository show that, OLA outperforms the individual base learner and majority voting when base learners are not capable enough for the given task. Bias-variance decomposition of the error indicates that OLA can reduce both bias and variance.

MSC:

68T05 Learning and adaptive systems in artificial intelligence

Software:

UCI-ml
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