Support Vector Machines have been a popular topic for quite some time now, and as they develop, a need for new methods of feature selection arises. This work presents various approaches SVM feature selection developped using new tools such as entropy measurement and K-medoid clustering. The work focuses on the use of one-class SVM's for wafer testing, with a numerical implementation in R.
@article{arxiv.1905.09653,
title = {New methods for SVM feature selection},
author = {Tangui Aladjidi and François Pasqualini},
journal= {arXiv preprint arXiv:1905.09653},
year = {2019}
}