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. Institute of Electrical and Electronics Engineers., pp. 2410-2415. (doi:10.1109/IJCNN.2001.938744).

Download

Full text not available from this repository.

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: Conference or Workshop Item (Paper)
Digital Object Identifier (DOI): doi:10.1109/IJCNN.2001.938744
Venue - Dates: IJCNN '01. International Joint Conference on Neural Networks, 2001, 2001-07-15 - 2001-07-17
Subjects:
ePrint ID: 22007
Date :
Date Event
2001Published
Date Deposited: 01 Mar 2007
Last Modified: 16 Apr 2017 22:53
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/22007

Actions (login required)

View Item View Item