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系統識別號 U0002-1707201313213300
中文論文名稱 線上拍賣詐騙偵測之屬性建構及挑選
英文論文名稱 Feature Construction and Feature Selection for Fraud Detection in Online Auctions
校院名稱 淡江大學
系所名稱(中) 資訊管理學系碩士班
系所名稱(英) Department of Information Management
學年度 101
學期 2
出版年 102
研究生中文姓名 劉禎翔
研究生英文姓名 Chen-Hsiang Liu
學號 600630031
學位類別 碩士
語文別 中文
口試日期 2013-06-22
論文頁數 43頁
口試委員 指導教授-張昭憲
委員-陳穆臻
委員-伍台國
委員-周清江
中文關鍵字 屬性挑選  屬性建構  詐騙偵測  線上拍賣 
英文關鍵字 Feature Selection  Feature Construction  Fraud Detection  Online Auction 
學科別分類
中文摘要 線上拍賣蘊含龐大商機,但詐騙者也開始混雜其中,讓消費者防不勝防。面對日益猖獗的線上拍賣詐騙,除了提醒交易者小心謹慎外,學者們提出各種詐騙偵測方法。一般而言,詐騙偵測的準確性與分類屬性集的效能息息相關。然而,前人大多使用經驗法則來設計屬性集,我們認為應有更系統化、更周全的考量。有鑒於此,本研究致力於發展詐騙偵測屬性集的挑選與建構方法,以提升詐騙偵測的準確性。為達成上述目標,首先,我們提出了一套基因式的屬性挑選方法,並設計了一套完備適應函數。在演化過程中,除了偵測準確率外,也同時顧及偵測成本的多寡,期能產生一組低成本、高效能的詐騙偵測屬性集。接著,本研究發展了一套語法演化式的屬性建構方法,以BNF為基礎,配合基因演算法,以各種不同方式組合原生屬性,以產生高效能的複合屬性。為了驗證提出方法的有效性,我們使用拍賣網站真實交易資料來進行實驗。實驗結果顯示,針對不同資料集,本研究提出的方法能有效縮減屬性集的大小,並獲得較佳的準確率。此外,語法演化後產生的新屬性也具有良好的偵測成功率,有助於總體準確的提升,與資料集維度的縮減。
英文摘要 Because of big commercial opportunity in online auctions, there are more and more fraudulent incidents. It is also difficult to let consumers aware fraudulent transactions. In the face of fraud in online auction, many scholars have proposed some fraud detection methods instead of reminding consumers to be careful. Generally, the success rate of fraud detection has a big relationship with the fraud detection feature set. Most of scholars designed their own feature set depends on experiences. In order to improving success rate of fraud detection and generating our feature set automatically by system. In this paper, we propose a BNF-based grammatical evolution method in feature construction and a genetic algorithms in feature selection for fraud detection. The grammatical evolution technique inspired by natural evolution is explored to detect fraudsters in online auctions. Moreover, we illustrate the effectiveness of our algorithm on a real dataset collected from a large online auction site Yahoo.
論文目次 第一章 緒論 1
第二章 背景知識與文獻探討 6
2.1 線上拍賣詐騙偵測屬性集 6
2.2 詐騙分類屬性之挑選 7
2.3 基因演算法 9
第三章 詐騙偵測屬性挑選方法 13
3.1 屬性集來源 13
3.2 基因式屬性挑選方法 15
3.2.1 屬性編碼 15
3.2.2 適應性函數設計及調整 15
3.3 加速演化過程方法 17
第四章 詐騙偵測屬性建構方法 18
4.1 以BNF表示新屬性 18
4.2 語法演化式的新屬性產生方法 21
第五章 實驗結果 26
5.1 實驗設定 26
5.2 實驗結果 27
5.3 屬性建構之詐騙偵測效能驗證 29
第六章 結論及未來展望 32
參考文獻 33
附錄A、屬性集資訊 39
附錄B、演化過程各代最佳偵測成功率(S-RATE)之變化 41

====================圖目錄====================

圖2 1:運算樹的交配 9
圖2 2:基因演算法流程圖 (蘇木春、張孝德 民86) 10
圖2 3:基因演算法單點交配示意圖 (蘇木春、張孝德 民86) 11
圖2 4:基因演算法單點突變示意圖 (蘇木春、張孝德 民86) 12
圖3 1:物種基因編碼及屬性選取之關係圖 15
圖4 1:以BNF法表示屬性 18
圖4 2:屬性樹範例 20
圖4 3:屬性運算樹之基因編碼 22
圖4 4:物種的複製 23
圖4 5:說明屬性單點交配之範例 23
圖4 6:交配後產生之新屬性(運算樹) 24
圖4 7:物種單點突變方式 25
圖4 8:突變後之交配池 25
圖4 9:將子代取出,進行下一代演化 25
圖5 1:將各種偵測屬性集應用於不同資料集之成功率綜合比較 28


====================表目錄====================

表3–1:不同大小屬性集與其準確率關係 14
表4–1:原生屬性集 19
表4–2:屬性樹節點定義 20
表4–3:物種複製個數設定範例 22
表5–1:不同資料來源與不同屬性集之準確率 27
表5–2:將各種偵測屬性集應用於不同資料集之成功率綜合比較 28
表5–3 : 資料來源及屬性集綜合比較(S-Rate) 29
表5–4:原生屬性集各屬性之成功率(S-Rate) 30
表5–5:語法演化產生之新屬性之成功率(S-Rate)前五名 31
表5–6:各屬性集成功率(S-Rate)比較 31
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