_PROBLEM CoEPrA-2006_Classification_001
 
_GROUP_NAME Gavin Cawley

_GROUP_MEMBERS
Gavin C. Cawley

_ADDRESS
School of Computing Sciences
University of East Anglia
Norwich, Norfolk, NR4 7TJ
United Kingdom

_MODELING_PROCEDURE
Modelling procedure is as follows:

The descriptors provided are first standardised, to have a mean of zero and
unit variance.  A least-squares support vector machine with an isotropic
radial basis function was used.  The regularisation and kernel parameters
were set by minimising the leave-one-out cross-validation estimate of the
sum-of-squares error using conjugate gradient decent.

A variaty of other approaches were investigated, using (nested) leave-one-out
cross-validation to determine the best.  The leave-one-out statistics for
this model are:

accuracy = 0.831461
auroc    = 0.871717 (using continuous scores)

No feature selection was performed (feature selection has often been observed
to reduce predictive accuracy, especially in the case of datasets with
more features than patterns it is probably not a good idea).  The
regularisation built into LS-SVMs is the only method used to control
over-fitting.

I have assumed that the test data are balanced and have adjusted the threshold
to obtain an equal number of positive and negative patterns.

The other files contained in the submission are:

predicted_class.txt - thr predicted class (+1/-1) of the test data
scores.txt - raw output of the LS-SVM - used to compute the true AUROC score
model - the parameters, weights and support vectors of the LS-SVM

_PREDICTION
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