Classification of Imbalanced Materials Data
This project investigates the problem of classifying and predicting fatigue crack initiation sites, through microstructure quantification in Austempered Ductile Iron. The aim of this work is to build data driven classifiers that provide enhanced understanding of a system through the ability to visualise input/output relationships, as well as providing good predictive performance for a set of imbalanced data.
Type: Postgraduate Research
Research Group: Information: Signals, Images, Systems Research Group
Theme: Machine Learning
Dates: ? to ?
- Federal Mogul
- Dr Philippa Reed
- Professor Steve R Gunn
- Gary Lee
You can edit the record for this project by visiting http://secure.ecs.soton.ac.uk/db/projects/editproj.php?project=236