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Classification of imbalanced data with transparent kernels

Classification of imbalanced data with transparent kernels
Classification of imbalanced data with transparent kernels
Two important issues regarding data driven classification are addressed here: model interpretation and unbalanced data. The aim is to build data driven classifiers that provide good predictive performance for a set of unbalanced data and enhance the understanding of a model by enabling input/output dependencies that exist to be visualised. The classification method is demonstrated on an unbalanced data set that describes fatigue crack initiation in automotive camshafts. To generate interpretable models, the support vector parsimonious analysis of variance technique is extended to the classification domain. The technique enables an additive decomposition of low dimensional kernel models to be recovered, enhancing model visualization. The standard averaging technique used to assess the performance of the model is inappropriate for unbalanced data. The geometric mean is used. These resulting components had low dimensions, and consequently can be visualized
1098-7576
2410-2415
Institute of Electrical and Electronics Engineers
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) Classification of imbalanced data with transparent kernels. In IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222). Institute of Electrical and Electronics Engineers. pp. 2410-2415 . (doi:10.1109/IJCNN.2001.938744).

Record type: Conference or Workshop Item (Paper)

Abstract

Two important issues regarding data driven classification are addressed here: model interpretation and unbalanced data. The aim is to build data driven classifiers that provide good predictive performance for a set of unbalanced data and enhance the understanding of a model by enabling input/output dependencies that exist to be visualised. The classification method is demonstrated on an unbalanced data set that describes fatigue crack initiation in automotive camshafts. To generate interpretable models, the support vector parsimonious analysis of variance technique is extended to the classification domain. The technique enables an additive decomposition of low dimensional kernel models to be recovered, enhancing model visualization. The standard averaging technique used to assess the performance of the model is inappropriate for unbalanced data. The geometric mean is used. These resulting components had low dimensions, and consequently can be visualized

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More information

Published date: 2001
Venue - Dates: International Joint Conference on Neural Networks, Washington DC, United States, 2001-07-15 - 2001-07-17

Identifiers

Local EPrints ID: 22007
URI: https://eprints.soton.ac.uk/id/eprint/22007
ISSN: 1098-7576
PURE UUID: 503e1340-9e31-4625-a873-226bee6551a5
ORCID for P.A.S. Reed: ORCID iD orcid.org/0000-0002-2258-0347

Catalogue record

Date deposited: 01 Mar 2007
Last modified: 20 Jul 2019 01:21

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