Affiliations: [a] Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran | [b] Department of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
Correspondence:
[*]
Corresponding author: Hedieh Sajedi, Department of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran. E-mail:[email protected]
Abstract: Ensemble pruning is an effective phase for ensemble methods to increase
the predictive performance and to decrease computational overheads. This
paper represents a novel ensemble pruning method named EPCTS (Ensemble
Pruning via Chained Tabu Searches). EPCTS applies a chain of tabu searches
for choosing models of ensemble progressively, until the best subset of them
is found. These tabu searches are customized with the proposed strategy
dubbed as ``Periodic Oblivion''. This strategy revokes interdict of all tabu
answers in the defined periods. EPCTS is compared with analogous ensemble pruning methods for pruning a
balanced heterogeneous ensemble, focusing on 20 problems. Experimental
results demonstrate that EPCTS leads to 2.65% averaged improvement in the
accuracy of pruned ensemble, compared to others. Further, EPCTS leads to
reduce computational overheads with dropping redundant and useless models
from the ensemble. Moreover, one of the crucial issues in the ensemble
learning field is making the decision to choose the type of base classifiers
constructing desired ensemble. Considering the importance of the issue and
due to the effectiveness of EPCTS in about 75% of datasets, EPCTS is
suggested as a general tool for recognizing the type of base classifiers.
