Hybrid parallel classifiers for semantic subspace learning
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
A fast subspace text categorization method using parallel classifiers
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
Hybrid classifiers based on semantic data subspaces for two-level text categorization
International Journal of Hybrid Intelligent Systems
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We propose a technique of localized multiple decision stumps. The ensemble consists of multiple decision stumps constructed locally by pseudorandomly selecting subsets of components of the feature vector, that is, decision stumps constructed in randomly chosen subspaces. The idea of the local ensemble is that although no single function works well globally, in any local region a function should be capable of doing the classification. We performed a comparison with other well known combining methods using decision stump as based learner, on standard benchmark datasets and the proposed method gave better accuracy.