New methods for SVM feature selection
arXiv:1905.09653v2
AI Analysis
This work addresses feature selection for SVMs in the specific domain of wafer testing, presenting incremental improvements.
The paper tackled the problem of feature selection for Support Vector Machines (SVMs) in wafer testing by developing new methods using entropy measurement and K-medoid clustering, resulting in a numerical implementation in R.
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.