PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

The Bias Variance Trade-Off in Bootstrapped Error Correcting Output Code Ensembles.
RS Smith and Terry Windeatt
In: MCS2009, Iceland(2009).

Abstract

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.

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6067
Deposited By:Terry Windeatt
Deposited On:08 March 2010