C4.5: programs for machine learning
C4.5: programs for machine learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hybrid neural plausibility networks for news agents
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Machine Learning
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Spoken language classification using hybrid classifier combination
International Journal of Hybrid Intelligent Systems
Discriminative learning of Bayesian network classifiers
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Constructing ensembles of symbolic classifiers
International Journal of Hybrid Intelligent Systems - Hybrid Intelligent systems in Ensembles
Survey of Improving Naive Bayes for Classification
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Boosting random subspace method
Neural Networks
Local Random Subspace Method for Constructing Multiple Decision Stumps
ICIFE '09 Proceedings of the 2009 International Conference on Information and Financial Engineering
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Co-training with relevant random subspaces
Neurocomputing
The ECIR 2010 large scale hierarchical classification workshop
ACM SIGIR Forum
Comparative performance evaluation of global-local hybrid ensemble
International Journal of Hybrid Intelligent Systems
Effectiveness of a hybrid pattern classifier for medical applications
International Journal of Hybrid Intelligent Systems
Fast training of multilayer perceptrons
IEEE Transactions on Neural Networks
Applications of Hybrid Extreme Rotation Forests for image segmentation
International Journal of Hybrid Intelligent Systems
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Many organizations are nowadays keeping their data in the form of multi-level categories for easier manageability. An example of this is the Reuters Corpus which has news items categorized in a hierarchy of up to five levels. The volume and diversity of documents available in such category hierarchies is also increasing daily. As such, it becomes difficult for a traditional classifier to efficiently handle multi-level categorization of such a varied document space. In this paper, we present hybrid classifiers involving various two-classifier and four-classifier combinations for two-level text categorization. We show that the classification accuracy of the hybrid combination is better than the classification accuracies of all the corresponding single classifiers. The constituent classifiers of the hybrid combination operate on different subspaces obtained by semantic separation of data. Our experiments show that dividing a document space into different semantic subspaces increases the efficiency of such hybrid classifier combinations. We further show that hierarchies with a larger number of categories at the first level benefit more from this general hybrid architecture.