Bias-variance analysis of ECOC and bagging using neural nets

Zor, Cemre and Windeatt, Terry and Yanıkoğlu, Berrin (2011) Bias-variance analysis of ECOC and bagging using neural nets. In: Okun, Oleg and Giorgio, Valentini and Re, Matteo, (eds.) Ensembles in Machine Learning Applications. Studies in Computational Intelligence, 373. Springer, Germany. ISBN 978-3-642-22909-1

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One of the methods used to evaluate the performance of ensemble classifiers is bias and variance analysis. In this chapter, we analyse bootstrap aggregating (bagging) and Error Correcting Output Coding (ECOC) ensembles using a biasvariance framework; and make comparisons with single classifiers, while having Neural Networks (NNs) as base classifiers. As the performance of the ensembles depends on the individual base classifiers, it is important to understand the overall trends when the parameters of the base classifiers -nodes and epochs for NNs-, are changed.We show experimentally on 5 artificial and 4 UCI MLR datasets that there are some clear trends in the analysis that should be taken into consideration while designing NN classifier systems.
Item Type: Book Section / Chapter
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Computer Science & Eng.
Faculty of Engineering and Natural Sciences
Depositing User: Berrin Yanıkoğlu
Date Deposited: 17 Aug 2011 10:47
Last Modified: 26 Apr 2022 08:25

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