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Outlier Detection Using Support Vector Machines
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 Title & Authors
Outlier Detection Using Support Vector Machines
Seo, Han-Son; Yoon, Min;
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 Abstract
In order to construct approximation functions for real data, it is necessary to remove the outliers from the measured raw data before constructing the model. Conventionally, visualization and maximum residual error have been used for outlier detection, but they often fail to detect outliers for nonlinear functions with multidimensional input. Although the standard support vector regression based outlier detection methods for nonlinear function with multidimensional input have achieved good performance, they have practical issues in computational cost and parameter adjustments. In this paper we propose a practical approach to outlier detection using support vector regression that reduces computational time and defines outlier threshold suitably. We apply this approach to real data examples for validity.
 Keywords
Outlier detection;support vector regression;practical approach;
 Language
Korean
 Cited by
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그럽 및 코크란 검정을 이용한 임상자료의 이상치 판단,손기철;신임희;

Journal of the Korean Data and Information Science Society, 2012. vol.23. 4, pp.657-663 crossref(new window)
2.
원양어선 조업 데이터의 혼합 극단분포를 이용한 이상점 탐색 연구,이정진;김재경;

응용통계연구, 2015. vol.28. 5, pp.847-858 crossref(new window)
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Outlier detection using Grubb test and Cochran test in clinical data, Journal of the Korean Data and Information Science Society, 2012, 23, 4, 657  crossref(new windwow)
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