Robustified least squares support vector classification | Zendy

Debruyne Michiel | Zendy; Serneels Sven | Zendy; Verdonck Tim | Zendy

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Robustified least squares support vector classification

Author(s) -

Debruyne Michiel,

Serneels Sven,

Verdonck Tim

Publication year - 2009

Publication title -

journal of chemometrics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.47

H-Index - 92

eISSN - 1099-128X

pISSN - 0886-9383

DOI - 10.1002/cem.1241

Subject(s) - support vector machine , outlier , artificial intelligence , pattern recognition (psychology) , computer science , least squares support vector machine , class (philosophy) , least squares function approximation , data mining , machine learning , mathematics , statistics , estimator

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 © 2009 John Wiley & Sons, Ltd.

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