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
Experiments with random projections for machine learning
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
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
Introduction to Information Retrieval
Introduction to Information Retrieval
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
Fast training of multilayer perceptrons
IEEE Transactions on Neural Networks
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Subspace learning is very important in today's world of information overload. Distinguishing between categories within a subset of a large data repository such as the web and the ability to do so in real time is critical for a successful search technique. The characteristics of data belonging to different domains are also varying widely. This merits the need for an architecture which caters to the differing characteristics of different data domains. In this paper we present a novel hybrid parallel architecture using different types of classifiers trained on different subspaces. We further compare the performance of our hybrid architecture with a single classifier and show that it outperforms the single classifier system by a large margin when tested with a variety of hybrid combinations. Our results show that subspace classification accuracy is boosted and learning time reduced significantly with this new hybrid architecture.