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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
d3d9a62f-fa96-4eb6-854d-a0196958caac
Crowder, Richard
ddeb646d-cc9e-487b-bd84-e1726d3ac023
Wills, Gary B.
3a594558-6921-4e82-8098-38cd8d4e8aa0
Phadermrod, Boonyarat
d3d9a62f-fa96-4eb6-854d-a0196958caac
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). (doi:10.7763/IJCEE).

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

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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: http://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

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Date deposited: 20 Oct 2014 11:12
Last modified: 15 Mar 2024 02:51

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Contributors

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

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