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Regression models for classification to enhance interpretability

Regression models for classification to enhance interpretability
Regression models for classification to enhance interpretability
Many classification techniques emphasize obtaining a good classification rate. In our view, a more important issue is to be able to interpret the underlying model. The aim of this work is to build data driven classifiers that provide enhanced understanding of a system through visualisation of I/O relationships, in addition to good predictive performance for a set of imbalanced data. The problem of data imbalance is addressed by incorporating a different misclassification cost for each class and an appropriate performance criteria. The Support vector Parismonious ANalysis Of VAriance (SUPANOVA) technique has been used successfully for regression problems in generating interpretable models. In this paper, we modify
SUPANOVA so that it can be applied to the domain of classification problems enabling a predictive model with a high degree of interpretability to be recovered. Here, the problem of classifying and predicting fatigue crack initiation sites, through microstructure quantification in Austempered Ductile Iron (ADI), is considered. SUPANOVA selects a sparse set of components from the model for easy visualisation. Results from the modified SUPANOVA technique provide good performance with 5 components selected out of the possible 512 as significant components. The components selected are consistent with prior knowledge of metallurgists working on the material. With this modelling knowledge, the key production and microstructure features can be identified to optimise automotive materials performance.
Lee, K.K.
9fec08f0-7782-4d2e-9313-73ed74fd8d53
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Gunn, S.R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Reed, P.A.S.
8b79d87f-3288-4167-bcfc-c1de4b93ce17
Lee, K.K.
9fec08f0-7782-4d2e-9313-73ed74fd8d53
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Gunn, S.R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Reed, P.A.S.
8b79d87f-3288-4167-bcfc-c1de4b93ce17

Lee, K.K., Harris, C.J., Gunn, S.R. and Reed, P.A.S. (2001) Regression models for classification to enhance interpretability. Proceedings of the 3rd International Conference: Intelligent Processing and Manufacturing of Materials, Canada. 29 Jul - 03 Aug 2001.

Record type: Conference or Workshop Item (Other)

Abstract

Many classification techniques emphasize obtaining a good classification rate. In our view, a more important issue is to be able to interpret the underlying model. The aim of this work is to build data driven classifiers that provide enhanced understanding of a system through visualisation of I/O relationships, in addition to good predictive performance for a set of imbalanced data. The problem of data imbalance is addressed by incorporating a different misclassification cost for each class and an appropriate performance criteria. The Support vector Parismonious ANalysis Of VAriance (SUPANOVA) technique has been used successfully for regression problems in generating interpretable models. In this paper, we modify
SUPANOVA so that it can be applied to the domain of classification problems enabling a predictive model with a high degree of interpretability to be recovered. Here, the problem of classifying and predicting fatigue crack initiation sites, through microstructure quantification in Austempered Ductile Iron (ADI), is considered. SUPANOVA selects a sparse set of components from the model for easy visualisation. Results from the modified SUPANOVA technique provide good performance with 5 components selected out of the possible 512 as significant components. The components selected are consistent with prior knowledge of metallurgists working on the material. With this modelling knowledge, the key production and microstructure features can be identified to optimise automotive materials performance.

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More information

Published date: August 2001
Venue - Dates: Proceedings of the 3rd International Conference: Intelligent Processing and Manufacturing of Materials, Canada, 2001-07-29 - 2001-08-03
Organisations: Electronic & Software Systems, Southampton Wireless Group

Identifiers

Local EPrints ID: 256444
URI: https://eprints.soton.ac.uk/id/eprint/256444
PURE UUID: 6bf73725-34f2-477d-ab00-f924b24de002
ORCID for P.A.S. Reed: ORCID iD orcid.org/0000-0002-2258-0347

Catalogue record

Date deposited: 29 Nov 2003
Last modified: 06 Jun 2018 13:09

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Contributors

Author: K.K. Lee
Author: C.J. Harris
Author: S.R. Gunn
Author: P.A.S. Reed ORCID iD

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