Outlier Detection for Support Vector Machine using Minimum Covariance Determinant Estimator

The purpose of this work is to identify the effective points in the performance of one of the important algorithms of data mining, namely support vector machine. The final classification decision is made based on the small portion of data called support vectors. Thus, existence of the atypical observations in the aforementioned points results in deviation from the correct decision. Therefore, the idea of Debruyne’s “outlier map” is employed in this work to identify the outlying points in the SVM classification problem. However, due to the computational reasons such as convenience and rapidity, a robust Mahalanobis distance based on the minimum covariance determinant estimator is utilized. This method has a good compatibility by the data with a low-dimensional structure. In addition to the classification accuracy, the margin width is used as the criterion for the performance assessment. The larger margin is more desired due to the higher generalization ability. It should be noted that by omission of the detected outliers using the suggested outlier map, the generalization ability and accuracy of SVM are increased. This leads to the conclusion that the proposed method is very efficient in identifying the outliers. The capability of recognizing the outlying and misclassified observations for this new version of outlier map is retained similar to the older version, which is tested on the simulated and real world data.