Comparison between Fisher's Ratio and Information Gain with SVM classifier for 3 levels of enthusiasm classification through face recognition

Abstract

The enthusiasm level of a person is an important measurement in real-world problems. This paper, therefore, presents face recognition classification for enthusiasm level, based on supervised machine learning, using Support Vector Machine (SVM) as a classifier, with a one-vs-one method because the data consists of more than two classes. In addition, Fisher's Ratio and Information Gain are applied in the selection of contributive features, and the goals were to present an accuracy comparison between SVM and Fisher's Ratio, as wells as with Information Gain, and the results showed the accuracy at 88,89%, and 80,95238%, respectively. This indicates the combination of SVM with Fisher Ratio to be better.