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An efficient ordering-based ensemble pruning algorithm via dynamic programming
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DOI：10.1007/s10489-015-0729-z.png)
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Abstract
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Although ordering-based pruning algorithms possess relatively high efficiency, there remains room for further improvement. To this end, this paper describes the combination of a dynamic programming technique with the ensemble-pruning problem. We incorporate dynamic programming into the classical ordering-based ensemble-pruning algorithm with complementariness measure (ComEP), and, with the help of two auxiliary tables, propose a reasonably efficient dynamic form, which we refer to as ComDPEP. To examine the performance of the proposed algorithm, we conduct a series of simulations on four benchmark classification datasets. The experimental results demonstrate the significantly higher efficiency of ComDPEP over the classic ComEP algorithm. The proposed ComDPEP algorithm also outperforms two other state-of-the-art ordering-based ensemble-pruning algorithms, which use uncertainty weighted accuracy and reduce-error pruning, respectively, as their measures. It is noteworthy that, the effectiveness of ComDPEP is just the same with that of the classical ComEP algorithm.
Keywords:
Neural network ensemble
Ensemble pruning algorithm
Dynamic programming
Ordering-based ensemble pruning algorithm with complementariness measure (ComEP)
Ordering-based ensemble pruning algorithm based on the complementariness measure via dynamic programming (ComDPEP)
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