_PROBLEM CoEPrA-2006_Classification_003

_GROUP_NAME Gavin Cawley

_GROUP_MEMBERS
Gavin Cawley

_ADDRESS
School of Computing Science
University of East Anglia
Norwich NR4 7TJ, U. K.

_MODELING_PROCEDURE

The training data are first normalised to lie in the range
[-1,+1].  A least-squares support vector machine with an
isotropic Gaussian kernel is then used to make the
predictions.  A nested leave-one-out cross-validation
procedure is used for model selection and performance
evaluation.  The threshold is set so as to maximise the
Matthew's correlation coefficient.

Leave-one-out cross-validation results:

Accuracy              = 0.676692
AUROC (raw scores)    = 0.695613
AUROC (binary scores) = 0.452284
MCC                   = 0.359085

Something is likely to be wrong if the test scores are
significantly different.  Having said that, there are a
very large number of patterns for which the raw output
is very near zero, so only a small change in the threshold
(in this case it is left at zero) can potentially make a
large change in any statistic that depends on the threshold,
such as the Matthew's correlation coefficient (i.e. IMHO
luck is not a negligible factor in deciding the ranking).

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