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Approximation of Ensemble Boundary using Spectral Coefficients

Windeatt, Terry, Zor, Cemre and Camgöz, Necati Cihan (2018) Approximation of Ensemble Boundary using Spectral Coefficients IEEE Transactions on Neural Networks and Learning Systems.

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A spectral analysis of a Boolean function is proposed for ap- proximating the decision boundary of an ensemble of classifiers, and an in- tuitive explanation of computing Walsh coefficients for the functional ap- proximation is provided. It is shown that the difference between first and third order coefficient approximation is a good indicator of optimal base classifier complexity. When combining Neural Networks, experimental re- sults on a variety of artificial and real two-class problems demonstrate un- der what circumstances ensemble performance can be improved. For tuned base classifiers, first order coefficients provide performance similar to ma- jority vote. However, for weak/fast base classifiers, higher order coefficient approximation may give better performance. It is also shown that higher order coefficient approximation is superior to the Adaboost logarithmic weighting rule when boosting weak Decision Tree base classifiers.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Camgöz, Necati
Date : 23 August 2018
DOI : 10.1109/TNNLS.2018.2861579
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Boolean functions, boosting, decision trees, ensemble classifier, multilayer perceptrons, pattern analysis, spectral analysis, supervised learning
Depositing User : Melanie Hughes
Date Deposited : 02 Aug 2018 07:54
Last Modified : 14 Nov 2018 08:33

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