The University of Southampton
University of Southampton Institutional Repository

Attribute importance measure based on back-propagation neural network: an empirical study

Attribute importance measure based on back-propagation neural network: an empirical study
Attribute importance measure based on back-propagation neural network: an empirical study
Over the years, many different Importance-Performance Analysis (IPA) variations have emerged as it is a primary tool for analyzing customer satisfaction. One of the recent IPA variations is Back-Propagation Neural Network based Importance-Performance Analysis (BPNN based IPA) that utilizes BPNN to measure Importance. To investigate the performance of the BPNN based IPA, the authors compared two types of BPNN models that have one and multiple output neurons referred as BPNN (regression) and BPNN (classification) respectively, with Multiple Linear Regression (MLR). This comparison demonstrates that the BPNN (regression) does not outperform MLR in term of model accuracy and training time, yet BPNN (classification) is superior to MLR and BPNN (regression) in term of model accuracy and predictive power. This finding leads to a reconsideration of the BPNN model used in the present BPNN based IPA
back-propagation neural network, empirical comparison, importance-performance analysis
1793-8163
Phadermrod, Boonyarat
fcde4e03-7e97-4993-b5da-f61a2cc083a0
Crowder, Richard
ddeb646d-cc9e-487b-bd84-e1726d3ac023
Wills, Gary B.
3a594558-6921-4e82-8098-38cd8d4e8aa0
Phadermrod, Boonyarat
fcde4e03-7e97-4993-b5da-f61a2cc083a0
Crowder, Richard
ddeb646d-cc9e-487b-bd84-e1726d3ac023
Wills, Gary B.
3a594558-6921-4e82-8098-38cd8d4e8aa0

Phadermrod, Boonyarat, Crowder, Richard and Wills, Gary B. (2015) Attribute importance measure based on back-propagation neural network: an empirical study. International Journal of Computer and Electrical Engineering, 7 (2).

Record type: Article

Abstract

Over the years, many different Importance-Performance Analysis (IPA) variations have emerged as it is a primary tool for analyzing customer satisfaction. One of the recent IPA variations is Back-Propagation Neural Network based Importance-Performance Analysis (BPNN based IPA) that utilizes BPNN to measure Importance. To investigate the performance of the BPNN based IPA, the authors compared two types of BPNN models that have one and multiple output neurons referred as BPNN (regression) and BPNN (classification) respectively, with Multiple Linear Regression (MLR). This comparison demonstrates that the BPNN (regression) does not outperform MLR in term of model accuracy and training time, yet BPNN (classification) is superior to MLR and BPNN (regression) in term of model accuracy and predictive power. This finding leads to a reconsideration of the BPNN model used in the present BPNN based IPA

Full text not available from this repository.

More information

Published date: April 2015
Keywords: back-propagation neural network, empirical comparison, importance-performance analysis
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 370038
URI: https://eprints.soton.ac.uk/id/eprint/370038
ISSN: 1793-8163
PURE UUID: e2efffa7-f9c6-4fdf-b98d-55b0d8fda57b
ORCID for Gary B. Wills: ORCID iD orcid.org/0000-0001-5771-4088

Catalogue record

Date deposited: 20 Oct 2014 11:12
Last modified: 06 Jun 2018 13:03

Export record

Contributors

Author: Boonyarat Phadermrod
Author: Richard Crowder
Author: Gary B. Wills ORCID iD

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×