_PROBLEM CoEPrA-2006_Classification_004

_GROUP_NAME Gavin Cawley

_GROUP_MEMBERS
Gavin Cawley

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

_MODELLING_PROCEDURE

The training data are first standardised to have a zero mean
and unit variance.  A least-squares support vector machine
with a normalised quadratic 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 over 1000 random test
training splits of the data.

Leave-one-out cross-validation results:

Accuracy              = 0.729730
AUROC (raw scores)    = 0.738558
AUROC (binary scores) = 0.891876
MCC                   = 0.250231

The MCC as a function of the threshold is pretty flat (i.e.
it depends on the selection of the data - so there is no way
to do a meaningful selection for the threshold parameter).

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