政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/64484
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文筆數/總筆數 : 109952/140887 (78%)
造訪人次 : 46285208      線上人數 : 832
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 > 資訊管理學系 > 期刊論文 >  Item 140.119/64484


    請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/64484


    題名: Topological pattern discovery and feature extraction for fraudulent financial reporting
    作者: Huang, Shin-Ying;Tsaih, Rua-Huan;Yu, Fang
    黃馨瑩;蔡瑞煌;郁方
    貢獻者: 資管系
    關鍵詞: Unsupervised learning;Growing Hierarchical Self-Organizing Map;Data mining;Fraudulent financial reporting
    日期: 2014.07
    上傳時間: 2014-03-06 16:30:04 (UTC+8)
    摘要: Fraudulent financial reporting (FFR) involves conscious efforts to mislead others regarding the financial condition of a business. It usually consists of deliberate actions to deceive regulators, investors or the general public that also hinder systematic approaches from effective detection. The challenge comes from distinguishing dichotomous samples that have their major attributes falling in the same distribution. This study pioneers a novel dual GHSOM (Growing Hierarchical Self-Organizing Map) approach to discover the topological patterns of FFR, achieving effective FFR detection and feature extraction. Specifically, the proposed approach uses fraudulent samples and non-fraudulent samples to train a pair of dual GHSOMs under the same training parameters and examines the hypotheses for counterpart relationships among their subgroups taking advantage of unsupervised learning nature and growing hierarchical structures from GHSOMs. This study further presents (1) an effective classification rule to detect FFR based on the topological patterns and (2) an expert-competitive feature extraction mechanism to capture the salient characteristics of fraud behaviors. The experimental results against 762 annual financial statements from 144 public-traded companies in Taiwan (out of which 72 are fraudulent and 72 are non-fraudulent) reveal that the topological pattern of FFR follows the non-fraud-central spatial relationship, as well as shows the promise of using the topological patterns for FFR detection and feature extraction.
    關聯: Expert System with Applications, 41(9), 4360-4372
    資料類型: article
    DOI 連結: http://dx.doi.org/10.1016/j.eswa.2014.01.012
    DOI: 10.1016/j.eswa.2014.01.012
    顯示於類別:[資訊管理學系] 期刊論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    index.html0KbHTML21497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