Neural Network error weights to reduce false positive

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I have a classification scenario where two outputs differ significantly in importance. Type 1 errors, false positives, must be avoided. Type 2 errors, missed positives, are much less important. How can I structure my neural network to reflect this? Help train specifies EW can be: "a Nox1 cell array of scalar values defining relative network output importance"
Experimenting with EW = [0.1; 0.9] etc has not influenced the portion of false positives.

Accepted Answer

Greg Heath
Greg Heath on 13 Jan 2015
Edited: Greg Heath on 13 Jan 2015
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
  1 Comment
Ariel Liebman
Ariel Liebman on 13 Apr 2020
Hi Greg, this is the one I am trying to find your answers on comp.ai.neural-nets.

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