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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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主要な著者: Mohammed Rashid, Abdullah, Midi, Habshah, Dhhan, Waleed, Arasan, Jayanthi
フォーマット: 論文
言語:English
出版事項: Taylor and Francis 2021
オンライン･アクセス: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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spelling 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
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description 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.
format Article
author Mohammed Rashid, Abdullah
Midi, Habshah
Dhhan, Waleed
Arasan, Jayanthi
spellingShingle Mohammed Rashid, Abdullah
Midi, Habshah
Dhhan, Waleed
Arasan, Jayanthi
Detection of outliers in high-dimensional data using nu-support vector regression
author_facet 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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score 13.251813

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