Detection of outliers in high-dimensional data using nu-support vector regression

Abstract

Support Vector Regression (SVR) is gaining in popularity in the detection of outliers and classification problems in high-dimensional data (HDD) as this technique does not require the data to be of full rank. In real application, most of the data are of high dimensional. Classification of high-dimensional data is needed in applied sciences, in particular, as it is important to discriminate cancerous cells from non-cancerous cells. It is also imperative that outliers are identified before constructing a model on the relationship between the dependent and independent variables to avoid misleading interpretations about the fitting of a model. The standard SVR and the μ-ε-SVR are able to detect outliers; however, they are computationally expensive. The fixed parameters support vector regression (FP-ε-SVR) was put forward to remedy this issue. However, the FP-ε-SVR using ε-SVR is not very successful in identifying outliers. In this article, we propose an alternative method to detect outliers i.e. by employing nu-SVR. The merit of our proposed method is confirmed by three real examples and the Monte Carlo simulation. The results show that our proposed nu-SVR method is very successful in identifying outliers under a variety of situations, and with less computational running time.

Keywords:

• High-dimensional data
• outliers
• robustness
• statistical learning theory
• support vector regression

Acknowledgments

The authors would like to thank editors and anonymous reviewers for their careful reading and helpful remarks.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The present research was partially supported by the Universiti Putra Malaysia Grant under Putra Grant (GPB) with project number [grant number GPB/2018/9629700].