政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/118191

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 109953/140892 (78%)
Visitors : 46227419 Online Users : 590

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

政大典藏 > College of Commerce > Department of Statistics > Periodical Articles > Item 140.119/118191

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/118191

Title:	Dimension Reduction of High-Dimensional Datasets Based on Stepwise SVM
Authors:	周珮婷 Chou, Elizabeth P. Ko, Tzu-Wei
Contributors:	統計系
Date:	2017-11
Issue Date:	2018-07-03 15:05:09 (UTC+8)
Abstract:	The current study proposes a dimension reduction method, stepwise support vector machine (SVM), to reduce the dimensions of large p small n datasets. The proposed method is compared with other dimension reduction methods, namely, the Pearson product difference correlation coefficient (PCCs), recursive feature elimination based on random forest (RF-RFE), and principal component analysis (PCA), by using five gene expression datasets. Additionally, the prediction performance of the variables selected by our method is evaluated. The study found that stepwise SVM can effectively select the important variables and achieve good prediction performance. Moreover, the predictions of stepwise SVM for reduced datasets was better than those for the unreduced datasets. The performance of stepwise SVM was more stable than that of PCA and RF-RFE, but the performance difference with respect to PCCs was minimal. It is necessary to reduce the dimensions of large p small n datasets. We believe that stepwise SVM can effectively eliminate noise in data and improve the prediction accuracy in any large p small n dataset.
Relation:	arXiv:1711.03346v1
Data Type:	article
Appears in Collections:	[Department of Statistics] Periodical Articles

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML2	464	View/Open

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

Loading...