Detection of outliers in high-dimensional data using nu-support vector regression
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-dimen...
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Taylor and Francis
2021
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my.upm.eprints.966392023-01-11T07:07:42Z http://psasir.upm.edu.my/id/eprint/96639/ Detection of outliers in high-dimensional data using nu-support vector regression Mohammed Rashid, Abdullah Midi, Habshah Dhhan, Waleed Arasan, Jayanthi 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. Taylor and Francis 2021 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/96639/1/ABSTRACT.pdf Mohammed Rashid, Abdullah and Midi, Habshah and Dhhan, Waleed and Arasan, Jayanthi (2021) Detection of outliers in high-dimensional data using nu-support vector regression. Journal of Applied Statistics, 49 (10). pp. 1-20. ISSN 0266-4763; ESSN: 1360-0532 https://www.tandfonline.com/doi/abs/10.1080/02664763.2021.1911965?journalCode=cjas20 10.1080/02664763.2021.1911965 |
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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. |
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Article |
author |
Mohammed Rashid, Abdullah Midi, Habshah Dhhan, Waleed Arasan, Jayanthi |
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Mohammed Rashid, Abdullah Midi, Habshah Dhhan, Waleed Arasan, Jayanthi Detection of outliers in high-dimensional data using nu-support vector regression |
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Mohammed Rashid, Abdullah Midi, Habshah Dhhan, Waleed Arasan, Jayanthi |
author_sort |
Mohammed Rashid, Abdullah |
title |
Detection of outliers in high-dimensional data using nu-support vector regression |
title_short |
Detection of outliers in high-dimensional data using nu-support vector regression |
title_full |
Detection of outliers in high-dimensional data using nu-support vector regression |
title_fullStr |
Detection of outliers in high-dimensional data using nu-support vector regression |
title_full_unstemmed |
Detection of outliers in high-dimensional data using nu-support vector regression |
title_sort |
detection of outliers in high-dimensional data using nu-support vector regression |
publisher |
Taylor and Francis |
publishDate |
2021 |
url |
http://psasir.upm.edu.my/id/eprint/96639/1/ABSTRACT.pdf http://psasir.upm.edu.my/id/eprint/96639/ https://www.tandfonline.com/doi/abs/10.1080/02664763.2021.1911965?journalCode=cjas20 |
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