AN EXTENSION OF THE OUTLIER MAP FOR VISUALIZING THE CLASSIFICATION RESULTS OF THE MULTI-CLASS SUPPORT VECTOR MACHINE

Abstract

The main objective of this study is a graphical display of the results of the high (as well as the low) dimensional multi-class support vector machine classification. Additionally, we will visually be able to detect the outliers and misclassified observations by using this graphical tool. The “outlier map” as a successful graphical outlier detection tool of robust statistics is extended in this paper. In fact, this is a bilateral extension concerning the misclassified and outlying observations recognition. The most important feature of this extension is creating two types of discriminative boundaries to segregate the data and detect the outlying observations. For this purpose, we employed the simple but efficient concept of the “confidence interval”, which is computed for the mean of decision function of support vector machine and then, “thresholding” technique. After that, the efficiency of the outlier map in terms of the preciseness of the correct outlier identification has been tested by the classification accuracy. Moreover, we deployed the margin width “before” and “after” outlier detection as the other criterion to assess the preciseness of the correct outlier identification. We conducted an empirical study based on the proposed method on the simulated and several well-known real datasets. It shows the effectiveness of our proposed method by increasing the “margin width” and gaining a higher classification accuracy.