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Comparison of neural networks and discriminant analysis in predicting forest cover types
Comparison of neural networks and discriminant analysis in predicting forest cover types
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AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
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IEEE Transactions on Information Theory
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ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
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Computer Communications
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ECML'05 Proceedings of the 16th European conference on Machine Learning
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In this paper, we propose a general framework for distributed boosting intended for efficient integrating specialized classifiers learned over very large and distributed homogeneous databases that cannot be merged at a single location. Our distributed boosting algorithm can also be used as a parallel classification technique, where a massive database that cannot fit into main computer memory is partitioned into disjoint subsets for a more efficient analysis. In the proposed method, at each boosting round the classifiers are first learned from disjoint datasets and then exchanged amongst the sites. Finally the classifiers are combined into a weighted voting ensemble on each disjoint data set. The ensemble that is applied to an unseen test set represents an ensemble of ensembles built on all distributed sites. In experiments performed on four large data sets the proposed distributed boosting method achieved classification accuracy comparable or even slightly better than the standard boosting algorithm while requiring less memory and less computational time. In addition, the communication overhead of the distributed boosting algorithm is very small making it a viable alternative to the standard boosting for large-scale databases.