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On Selecting Additional Classifier Models in
Double Bagging Type Ensemble Method
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F. Zaman, M. Mesbah, and H. Hirose
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The 2010 International Conference on Computational Science and
Its Applications (ICCSA 2010), ICCSA2010, PartIV,March 23-26,
2010. Lecture Notes in Computer Science, 2010, Volume 6019, 199-208,
DOI: 10.1007/978-3-642-12189-0_18, Springer 2010.
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Bagging is a parallel ensemble method, where an additional classifier
model is trained on the out-of-bag samples and then the posteriori
class probabilities of this additional classifier are added with
the inbag samples to train a decision tree classifier. The subsampled
version of double bagging depend on two hyper parameters, subsample
ratio (SSR) and an additional classifier. In this paper we have
proposed an embedded cross-validation based selection technique
to select one of these parameters automatically. This selection
technique builds different ensemble classifier models with each
of these parameter values (keeping another fixed) during the training
phase of the ensemble method and finally select the one with the
highest accuracy. We have used four additional classifier models,
Radial Basis Support Vector Machine (RSVM), Linear Support Vector
Machine (LSVM), Nearest Neighbor Classifier (5-NN and 10-NN) with
five subsample ratios (SSR), 0.1, 0.2, 0.3, 0.4 and 0.5. We have
reported the performance of the subsampled double bagging ensemble
with these SSRs with each of these additional classifiers. In our
experiments we have used UCI benchmark datasets. The results indicate
that LSVM has superior performance as an additional classifiers
in enhancing the predictive power of double bagging, where as with
SSR 0.4 and 0.5 double bagging has better performance, than with
other SSRs. We have also compared the performance of these resulting
ensemble methods with Bagging, Adaboost, Double Bagging (original)
and Rotation Forest. Experimental results show that the performance
of the resulting subsampled double bagging ensemble is better than
these ensemble methods. |
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Times Cited in Web of Science: 1
Times Cited in Google Scholar: 1
Cited in Books:
Inspec:
WoS: KNOWLEDGE ENGINEERING
REVIEW 巻: 29 号: 1 ページ: 78-100 発行: JAN 2014
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