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|a 10.1080/02664763.2020.1849053
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|a eng
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1 |
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|a Fang, Kuangnan
|e verfasserin
|4 aut
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|a Structured sparse support vector machine with ordered features
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|c 2022
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|a Text
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|a Date Revised 16.07.2022
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|a published: Electronic-eCollection
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|a Citation Status PubMed-not-MEDLINE
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|a © 2020 Informa UK Limited, trading as Taylor & Francis Group.
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|a In the application of high-dimensional data classification, several attempts have been made to achieve variable selection by replacing the ℓ 2 -penalty with other penalties for the support vector machine (SVM). However, these high-dimensional SVM methods usually do not take into account the special structure among covariates (features). In this article, we consider a classification problem, where the covariates are ordered in some meaningful way, and the number of covariates p can be much larger than the sample size n. We propose a structured sparse SVM to tackle this type of problems, which combines the non-convex penalty and cubic spline estimation procedure (i.e. penalizing second-order derivatives of the coefficients) to the SVM. From a theoretical point of view, the proposed method satisfies the local oracle property. Simulations show that the method works effectively both in feature selection and classification accuracy. A real application is conducted to illustrate the benefits of the method
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|a Journal Article
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|a Structured sparse
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| 650 |
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|a local oracle property
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| 650 |
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|a support vector machine
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| 650 |
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|a variable selection
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| 700 |
1 |
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|a Wang, Peng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Xiaochen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Qingzhao
|e verfasserin
|4 aut
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|i Enthalten in
|t Journal of applied statistics
|d 1991
|g 49(2022), 5 vom: 09., Seite 1105-1120
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|x 0266-4763
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| 773 |
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|g volume:49
|g year:2022
|g number:5
|g day:09
|g pages:1105-1120
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|u http://dx.doi.org/10.1080/02664763.2020.1849053
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