Authors: Paul J. Roberts; Richard J. Mitchell; Virginie F. Ruiz; J. Mark Bishop
Addresses: @UK plc, 5 Jupiter House, Calleva Park, Aldermaston, RG7 8NN, UK ' School of Systems Engineering, University of Reading, Whiteknights, Reading, Berks RG6 6AY, UK ' School of Systems Engineering, University of Reading, Whiteknights, Reading, Berks RG6 6AY, UK ' Department of Computing, Goldsmiths, University of London, New Cross, London, SE14 6NW, UK
Abstract: Three coupled knowledge transfer partnerships used pattern recognition techniques to produce an e-procurement system which, the National Audit Office reports, could save the National Health Service £500 m per annum. An extension to the system, GreenInsight, allows the environmental impact of procurements to be assessed and savings made. Both systems require suitable products to be discovered and equivalent products recognised, for which classification is a key component. This paper describes the innovative work done for product classification, feature selection and reducing the impact of mislabelled data.
Keywords: product classification; feature selection; noise reduction; e-procurement; electronic procurement; online procurement; knowledge transfer; pattern recognition; National Health Service; NHS; environmental impact; mislabelled data; UK; United Kingdom.
International Journal of Applied Pattern Recognition, 2014 Vol.1 No.3, pp.298 - 314
Received: 28 Mar 2013
Accepted: 30 Aug 2013
Published online: 19 Nov 2014 *