Neural Network error weights to reduce false positive
1 vue (au cours des 30 derniers jours)
Afficher commentaires plus anciens
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.
0 commentaires
Réponse acceptée
Greg Heath
le 13 Jan 2015
Modifié(e) : Greg Heath
le 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 commentaire
Ariel Liebman
le 13 Avr 2020
Hi Greg, this is the one I am trying to find your answers on comp.ai.neural-nets.
Plus de réponses (0)
Voir également
Catégories
En savoir plus sur Pattern Recognition and Classification dans Help Center et File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!