政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152335
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文筆數/總筆數 : 115418/146445 (79%)
造訪人次 : 55138901      線上人數 : 358
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜尋範圍 查詢小技巧：
  • 您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料
    •  進階搜尋
    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/152335


    請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/152335


    題名: Topological Risk-Landscape in Metric-Free Categorical Database
    作者: 周珮婷
    Chou, Elizabeth P.;Hsieh, Fushing;Kao, Hong-Wei
    貢獻者: 統計系
    關鍵詞: Behavioral risk factor surveillance system (BRFSS);bipartite network heatmap;categorical exploratory data analysis (CEDA);complex system;conditional entropy
    日期: 2024-05
    上傳時間: 2024-07-17
    摘要: The Entropy-based Categorical Exploratory Data Analysis (CEDA) paradigm is elaborately refined to algorithmically explore the intricate high-order directional associative relational patterns within the heterogeneous chronical disease dynamics captured by Behavioral Risk Factor Surveillance System (BRFSS) database. Operating on this imbalanced categorical dataset represented fully by its metric-free high-dimensional histogram, our algorithms conduct data-driven computations to investigate chronic disease mechanisms across four sub-populations along the age-axis, culminating in comprehensive systemic understandings. Upon this categorical data-world, CEDA first recognizes the category-oriented 1D histogram as the simplest form of a piece of explainable information. Then, utilizing Kolmogorov’s randomness-proper-based reliability check, CEDA identifies and confirms collectives of 1D histograms as major feature-categories of varying orders within each sub-population. These confirmed major feature-categories’ binary memberships are then arranged into a subject-vs-feature-category bipartite network heatmap, revealing serial horizontal and vertical blocks framed by clusters of similar subjects characterized by individual-risk-landscapes (IRL) against clusters of structurally dependent major feature-categories. Based on such block-series, sub-population-specific disease mechanisms emerge as collective high-order interacting effects, elucidating directional associative relationships from study subjects’ topological neighborhoods to response-categories. Notably, the topological individual-risk-landscape offers profound insights into complex system dynamics and simultaneously exposes atypical subjects as explainable errors across all Machine Learning classifiers.
    關聯: IEEE Access, Vol.12, pp.66296-66318
    資料類型: article
    DOI 連結: https://doi.org/10.1109/ACCESS.2024.3398416
    DOI: 10.1109/ACCESS.2024.3398416
    顯示於類別:[統計學系] 期刊論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    index.html0KbHTML210檢視/開啟


    在政大典藏中所有的資料項目都受到原著作權保護.


    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 ©   - 回饋