Comparative Study of Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machine (SVM) In Dataset.

Abstract:

Classification techniques is an important factor in data analysis. Over the years, different classification method have been proposed for classification of dataset. In this paper, we compared three classifiers (LDA, QDA and SVM) in three imbalanced datasets (Iris, Pima and Glass data) and misclassification rate of the three classification method were compared. The experiments concentrated on analyzing the average misclassification rate among classifiers across the three dataset studied using the misforest imputation method to balance the dataset respectively. The results reveal that for the glass dataset, the QDA classifies the dataset better than the two other classification method studied, while for the iris and glass datasets, the LDA outperformed the other two classifiers studied. The conclusion in this study is that LDA have the least average misclassification error, followed by the QDA and then the SVM with an average misclassification rate of 0.2863.