English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113648/144635 (79%)
Visitors : 51628406      Online Users : 533
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
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version
    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/142020


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/142020


    Title: Network-based discriminant analysis for multiclassification
    Authors: 陳立榜
    Chen, Li-Pang
    Contributors: 統計系
    Keywords: F-score;Gaussian graphical models;Discriminant function;Multiclassification;Network structure;Precision matrix;Prediction
    Date: 2022-06
    Issue Date: 2022-09-21 11:45:35 (UTC+8)
    Abstract: Classification for multi-label responses, known as multiclassification, has been an important problem in supervised learning and has attracted our attention. In the framework of statistical learning, discriminant analysis is a powerful method to do multiclassification. With the increasing availability of complex data, it becomes more challenging to analyze them. One of the important features in complex data is the network structure, which is ubiquitous in high-dimensional data because of strong or weak correlations among variables. Although discriminant analysis is one of the supervised learning methods to deal with multiclassification and relevant extensions have been explored, little method has been available to handle multiclassification with network structures accommodated. To incorporate network structures in predictors and improve the accuracy of classification, we propose network-based linear discriminant analysis and network-based quadratic discriminant analysis in this paper. The main advantage of the proposed methods is to estimate the inverse of covariance matrices directly and do classification for multi-label responses instead of restricting on binary responses. In addition, the proposed methods are easy to compute and implement. Finally, numerical studies are conducted to assess the performance of the proposed methods, and numerical results verify that the proposed methods outperform their competitors.
    Relation: Journal of Classification, Vol.39, pp.410-431
    Data Type: article
    DOI 連結: https://doi.org/10.1007/s00357-022-09414-y
    DOI: 10.1007/s00357-022-09414-y
    Appears in Collections:[統計學系] 期刊論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML2236View/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 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.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback