Minimising added classification error using walsh coefficients

Windeatt, Terry and Zor, Cemre (2011) Minimising added classification error using walsh coefficients IEEE Transactions on Neural Networks, 22 (8). pp. 1334-1339.

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Abstract

Two-class supervised learning in the context of a classifier ensemble may be formulated as learning an incompletely specified Boolean function, and the associated Walsh coefficients can be estimated without knowledge of the unspecified patterns. Using an extended version of the Tumer-Ghosh model, the relationship between Added Classification Error and second order Walsh coefficients is established. In this paper, the ensemble is composed of Multi-layer Perceptron (MLP) base classifiers, with the number of hidden nodes and epochs systematically varied. Experiments demonstrate that the mean second order coefficients peak at the same number of training epochs as ensemble test error reaches a minimum.

Item Type:	Article
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :	Name Email ORCID Windeatt, Terry t.windeatt@surrey.ac.uk Zor, Cemre c.zor@surrey.ac.uk
Date :	2011
DOI :	10.1109/TNN.2011.2159513
Copyright Disclaimer :	© 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Depositing User :	Symplectic Elements
Date Deposited :	08 Feb 2012 15:10
Last Modified :	16 Jan 2019 16:28
URI:	http://epubs.surrey.ac.uk/id/eprint/37251

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