The classic approach to pattern recognition is to minimize Bayesian risk which, for c classes, is a double sum over classes of products of something like ( See a pattern recognition text for accurate details)
a priori class probabilities P(i)
input conditional class densities p(j,x)
misclassification costs C(i,j) or C(j,i)?
The message is you can choose the costs to bias the decisions any way you want.
I have many posts re this issue. Search
classification costs
in both the NEWSGROUP and ANSWERS as well as comp.ai.neural-nets.
Hope this helps.
Thank you for formally accepting my answer
Greg
