A fast ensemble pruning algorithm based on pattern mining process |
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Authors: | Qiang-Li Zhao Yan-Huang Jiang Ming Xu |
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Affiliation: | (1) School of Computer Science, National University of Defense Technology, Changsha, Hunan Province, People’s Republic of China |
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Abstract: | Ensemble pruning deals with the reduction of base classifiers prior to combination in order to improve generalization and
prediction efficiency. Existing ensemble pruning algorithms require much pruning time. This paper presents a fast pruning
approach: pattern mining based ensemble pruning (PMEP). In this algorithm, the prediction results of all base classifiers
are organized as a transaction database, and FP-Tree structure is used to compact the prediction results. Then a greedy pattern
mining method is explored to find the ensemble of size k. After obtaining the ensembles of all possible sizes, the one with the best accuracy is outputted. Compared with Bagging,
GASEN, and Forward Selection, experimental results show that PMEP achieves the best prediction accuracy and keeps the size
of the final ensemble small, more importantly, its pruning time is much less than other ensemble pruning algorithms. |
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Keywords: | Pattern mining based ensemble pruning FP-Tree Bagging Back-propagation neural network |
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