Abstract
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.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China under the Grant no. 61473150.
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Appendices
Appendix A: Formal procedure of the ComEP algorithm
The formal procedure of the ComEP algorithm is as follows.
Appendix B: Detailed computational rules for Table 2
The detailed computational rules for Table 2, viz. ClassVoteCounts, are as follows.
Appendix C: Formal procedure of the ComDPEP algorithm
The formal procedure of the ComDPEP algorithm is as follows.
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Dai, Q., Han, X. An efficient ordering-based ensemble pruning algorithm via dynamic programming. Appl Intell 44, 816–830 (2016). https://doi.org/10.1007/s10489-015-0729-z
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DOI: https://doi.org/10.1007/s10489-015-0729-z