A threshold varying bisection method for cost sensitive learning in neural networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a bisection method for varying classification threshold value for cost sensitive neural network learning. Using simulated data and different cost asymmetries, we test the proposed threshold varying bisection method and compare it with the traditional fixed-threshold method based neural network learning. The results of our experiments illustrate that the proposed threshold varying bisection method performs better than the traditional fixed-threshold method.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Pages1039-1044
Number of pages6
DOIs
StatePublished - Dec 1 2005
EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
Duration: Jul 31 2005Aug 4 2005

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2

Other

OtherInternational Joint Conference on Neural Networks, IJCNN 2005
CountryCanada
CityMontreal, QC
Period7/31/058/4/05

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'A threshold varying bisection method for cost sensitive learning in neural networks'. Together they form a unique fingerprint.

Cite this