Tech Report announcement

[drl@eng.cam.ac.uk]

The following technical report is available by anonymous ftp from the
archive of the Speech, Vision and Robotics Group at the Cambridge
University Engineering Department.

   Limits on the discrimination possible with discrete valued data,
           with application to medical risk prediction

D. R. Lovell, C. R. Dance, M. Niranjan, R. W.  Prager and K. J. Dalton
             Technical Report CUED/F-INFENG/TR243

           Cambridge University Engineering Department 
                       Trumpington Street 
                        Cambridge CB2 1PZ 
                            England 


                            Abstract

  We describe an upper bound on the {\em accuracy} (in the ROC sense)
  attainable in two-alternative forced choice risk prediction, for a
  specific set of data represented by discrete features. By accuracy, we
  mean the probability that a risk prediction system will correctly rank a
  randomly chosen high risk case and a randomly chosen low risk case.

  We also present methods for estimating the maximum accuracy we can
  expect to attain using a given set of discrete features to represent
  data sampled from a given population.

  These techniques allow an experimenter to calculate the maximum
  performance that could be achieved, without having to resort to
  applying specific risk prediction methods. Furthermore, these
  techniques can be used to rank discrete features in order of their
  effect on maximum attainable accuracy.

************************ How to obtain a copy ************************

Via FTP:

unix> ftp svr-ftp.eng.cam.ac.uk
Name: anonymous
Password: (type your email address)
ftp> cd reports
ftp> binary
ftp> get lovell_tr243.ps.Z
ftp> quit
unix> uncompress NAME_tr243.ps.Z
unix> lpr lovell_tr243.ps (or however you print PostScript)

No hardcopies available.