# New methods for SVM feature selection

## TL;DR

This paper introduces novel SVM feature selection methods utilizing entropy and K-medoid clustering, focusing on one-class SVMs for wafer testing, implemented in R.

## Contribution

It presents new feature selection techniques for SVMs based on entropy and clustering, tailored for one-class SVM applications in wafer testing.

## Key findings

- Effective feature selection methods for SVMs demonstrated
- Improved wafer testing accuracy using proposed techniques
- Implementation in R facilitates practical adoption

## Abstract

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.