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Introducing randomness into greedy ensemble pruning algorithms

Authors :
Meiling Li
Qun Dai
Source :
Applied Intelligence. 42:406-429
Publication Year :
2014
Publisher :
Springer Science and Business Media LLC, 2014.

Abstract

As is well known, the Greedy Ensemble Pruning (GEP) algorithm, also called the Directed Hill Climbing Ensemble Pruning (DHCEP) algorithm, possesses relatively good performance and high speed. However, because the algorithm only explores a relatively small subspace within the whole solution space, it often produces suboptimal solutions of the ensemble pruning problem. Aiming to address this drawback, in this work, we propose a novel Randomized GEP (RandomGEP) algorithm, also called the Randomized DHCEP (RandomDHCEP) algorithm, that effectively enlarges the search space of the classical DHCEP while maintaining the same level of time complexity with the help of a randomization technique. The randomization of the classical DHCEP algorithm achieves a good tradeoff between the effectiveness and efficiency of ensemble pruning. Besides, the RandomDHCEP algorithm naturally inherits the two intrinsic advantages that a randomized algorithm usually possesses. First, in most cases, its running time or space requirements are smaller than well-behaved deterministic ensemble pruning algorithms. Second, it is easy to comprehend and implement. Experimental results on three benchmark classification datasets verify the practicality and effectiveness of the RandomGEP algorithm.

Details

ISSN :
15737497 and 0924669X
Volume :
42
Database :
OpenAIRE
Journal :
Applied Intelligence
Accession number :
edsair.doi...........cf5d26496bc061e495ee139df7015a77
Full Text :
https://doi.org/10.1007/s10489-014-0605-2