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Reduced ensemble size stacking [ensemble learning]

16th IEEE International Conference on Tools with Artificial Intelligence, 2005
We investigate an algorithmic extension to the technique of stacked regression that prunes the size of a homogeneous ensemble set based on a consideration of the accuracy and diversity of the set members. We show that the pruned ensemble set is as accurate on average over the data-sets tested as the nonpruned version, which provides benefits in terms ...
N. Rooney, D. Patterson, C. Nugent
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Spatial Ensemble Learning

2017
This chapter introduces a novel ensemble learning framework called spatial ensemble, which is used to classify heterogeneous spatial data with class ambiguity. Class ambiguity refers to the phenomenon whereby samples with similar features belong to different classes at different locations (e.g., spectral confusion between different thematic classes in ...
Zhe Jiang, Shashi Shekhar
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Ensemble Learning Approaches

2015
As mentioned in Chap. 1, ensemble learning is helpful to improve overall accuracy of classification. This chapter introduces three approaches of ensemble learning namely, parallel learning, sequential learning and hybrid learning. In particular, some popular methods for ensemble learning, such as Bagging and Boosting, are illustrated in detail.
Han Liu, Alexander Gegov, Mihaela Cocea
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Ensemble Learning