Conference paper
Selection of objective function for imbalanced classification: an industrial case study
In this article we discuss the issue of selecting suitable objective function for Genetic Algorithm to solve an imbalanced classification problem. More precisely, first we discuss the need of specialized objective function to solve a real classification problem from our industrial partner and then we compare the results of our proposed objective function with commonly used candidates to serve this purpose.
Our comparison is based on the analysis of real data collected during the quality control stages of the manufacturing process.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2017 |
| Pages: | 1-4 |
| Proceedings: | 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation |
| ISBN: | 1509065040 , 1509065059 , 1509065067 , 9781509065042 , 9781509065059 and 9781509065066 |
| ISSN: | 19460759 and 19460740 |
| Types: | Conference paper |
| DOI: | 10.1109/ETFA.2017.8396223 |
| ORCIDs: | Kulahci, Murat |
Correlation Data mining Genetic Algorithm Genetic algorithms Linear programming Manufacturing processes Optimization Quality control genetic algorithms imbalanced classification problem industrial case study manufacturing process manufacturing processes objective function pattern classification production engineering computing quality control real data collection analysis
