English  |  正體中文  |  简体中文  |  Post-Print筆數 : 11 |  Items with full text/Total items : 88531/118073 (75%)
Visitors : 23458460      Online Users : 83
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/118191
    Please use this identifier to cite or link to this item: http://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:[統計學系] 期刊論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML127View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    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。
    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。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback