Journal of Chemometrics vol:23 issue:9-10 pages:479-486
Support vector machine (SVM) algorithms are a popular class of techniques to perform classification. However, outliers in the data can result in bad global misclassification percentages. In this paper, we propose a method to identify such outliers in the SVM framework. A specific robust classification algorithm is proposed adjusting the least squares SVM (LS-SVM). This yields better classification performance for heavily tailed data and data containing outliers. Copyright (C) 2009 John Wiley & Sons, Ltd.