Classification of imbalanced data with transparent kernel


Lee, K.K., Harris, C.J., Gunn, S.R. and Reed, P.A.S. (2001) Classification of imbalanced data with transparent kernel. In, Proceedings of IJCNN '01. International Joint Conference on Neural Networks, 2001. IJCNN '01. International Joint Conference on Neural Networks, 2001 Piscataway, USA, Institute of Electrical and Electronics Engineers, 2410-2415. (doi:10.1109/IJCNN.2001.938744).

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Description/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

Item Type: Book Section
Related URLs:
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Divisions: University Structure - Pre August 2011 > School of Engineering Sciences
University Structure - Pre August 2011 > School of Electronics and Computer Science
ePrint ID: 22007
Date Deposited: 01 Mar 2007
Last Modified: 27 Mar 2014 18:11
Publisher: Institute of Electrical and Electronics Engineers
URI: http://eprints.soton.ac.uk/id/eprint/22007

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