Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Ensemble pruning for text categorization based on data partitioning
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
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Recent studies show that ensemble pruning works as effective as traditional ensemble of classifiers (EoC). In this study, we analyze how ensemble pruning can improve text categorization efficiency in time-critical real-life applications such as news portals. The most crucial two phases of text categorization are training classifiers and assigning labels to new documents; but the latter is more important for efficiency of such applications. We conduct experiments on ensemble pruning-based news article categorization to measure its accuracy and time cost. The results show that our heuristics reduce the time cost of the second phase. Also we can make a trade-off between accuracy and time cost to improve both of them with appropriate pruning degrees.