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The Bias Variance Trade-Off in Bootstrapped Error Correcting Output Code Ensembles

Smith, RS and Windeatt, T (2009) The Bias Variance Trade-Off in Bootstrapped Error Correcting Output Code Ensembles In: 8th International Workshop on Multiple Classifier Systems, 2009-06-10 - 2009-06-12, Univ Iceland, Reykjavik, ICELAND.

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By performing experiments on publicly available multi-class datasets we examine the effect of bootstrapping on the bias/variance behaviour of error-correcting output code ensembles. We present evidence to show that the general trend is for bootstrapping to reduce variance but to slightly increase bias error. This generally leads to an improvement in the lowest attainable ensemble error, however this is not always the case and bootstrapping appears to be most useful on datasets where the non-bootstrapped ensemble classifier is prone to overfitting.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Smith, RS
Windeatt, T
Date : 2009
DOI : 10.1007/978-3-642-02326-2_1
Contributors :
ContributionNameEmailORCID, JA, J, F BERLIN,
Depositing User : Symplectic Elements
Date Deposited : 22 Sep 2011 10:34
Last Modified : 31 Oct 2017 14:08

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