UPM Institutional Repository

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


Mohammed Rashid, Abdullah and Midi, Habshah and Waleed Dhhan and Arasan, Jayanthi (2021) Detection of outliers in high dimensional data using NU-support vector regression. Journal of Applied Statistics, 49 (10). pp. 2550-2569. ISSN 0266-4763; ESSN: 1360-0532


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.

Download File

Full text not available from this repository.

Additional Metadata

Item Type: Article
Divisions: Faculty of Science
Institute for Mathematical Research
DOI Number: https://doi.org/10.1080/02664763.2021.1911965
Publisher: Taylor and Francis
Keywords: High-dimensional data; Outliers; Robustness; Statistical learning theory; Support vector regression
Depositing User: Ms. Nur Faseha Mohd Kadim
Date Deposited: 14 Jul 2023 08:55
Last Modified: 14 Jul 2023 08:55
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1080/02664763.2021.1911965
URI: http://psasir.upm.edu.my/id/eprint/100919
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item