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Title |
Observational Learning Algorithm for Heterogeneous Ensembles |
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Author |
Yu Fan, Yang Li-Ying, Qin Zheng |
| Citation |
Vol. 6 No. 3 pp. 53-56 |
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Abstract |
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 focused on OLA for homogeneous ensembles, such as neural networks ensembles. In this paper, OLA for heterogeneous ensembles was proposed, which is a process with three steps: training, observing, and retraining.. 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. |
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Keywords |
Observational Learning, Social Learning, Classifiers Ensemble, Heterogeneous Ensemble |
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