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系統識別號 U0002-1502201221323800
中文論文名稱 使用降低規則相依問題影響來改善關聯式分類效能
英文論文名稱 Improving the performance of association classifiers by reducing the impact of rule dependency problem
校院名稱 淡江大學
系所名稱(中) 資訊工程學系博士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 100
學期 1
出版年 101
研究生中文姓名 陳智揚
研究生英文姓名 Chih-Yang Chen
學號 895410263
學位類別 博士
語文別 中文
口試日期 2012-01-03
論文頁數 121頁
口試委員 指導教授-蔣璿東
委員-謝楠楨
委員-陳俊豪
委員-王亦凡
委員-葛煥昭
委員-蔣璿東
中文關鍵字 關聯式分類  文件分類  規則相依 
英文關鍵字 Association Classification  Text Classification  Rule Dependency 
學科別分類 學科別應用科學資訊工程
中文摘要 關聯式分類法(Associative Classification, AC)在規則排序(Ranking)時會因為分類的方式不同而有所差異,但基本上,都是依照信賴度由高至低排序,所以在進行分類時,也都是先利用信賴度高的規則做分類。由於某些關聯式規則之間會有規則相依問題(rule dependency problem),對這些規則而言,執行的先後順序會影響到這些規則對尚未被分類資料的信賴度;由於rule dependency problem會造成規則信賴度的改變,所以對尚未被分類資料而言,目前關聯式分類法都實際上並未完全信賴度的由高低來進行分類,進而影響最後分類的結果。現有的關聯式分類法在進行分類時,都沒有考慮 Rule dependency problem。主要是因為訓練文件有可能會產生不同類別的規則,在分類時,也可能會因為不同的規則被分類至不同的類別,因此哪一條規則先執行的確會產生不同的分類結果。但對有 n 條規則的 AC 而言,規則有 n! 種執行順序,所以要解決rule dependency problem (找尋最佳規則執行順序) 將是一個非常耗時的工作。所以本研究主要探討關聯式分類器中Rule Dependency Problem的問題,並提出不同的多項式時間(polynomial time)排序演算法來設定分類器中規則的執行順序,以降低規則相依問題對分類結果的影響,進而提昇關聯式分類器的分類準確度。
英文摘要 Since the dependence of rules may affect the confidences of rules, the execution order of the remaining rules is not ranked by the actual confidence of rules to the unclassified data, which will directly influence the classification accuracy of the associative classifier. However, finding the optimal execution order of CARs is a combinational problem, it is a very time consuming process. In this project, instead of finding the optimal execution order of CARs, we plan to propose different algorithms to re-rank the execution order of CARs to reduce the influence of the rule dependency problem and improve the classification accuracy of the associative classifier. For resolving the rule dependency problem, the number of executing ranking for N rules should be N!. As a result, finding out the optimal rule-executing ranking is a time consuming task. Therefore, instead of finding the optimal execution order of CARs, in this paper, we propose a polynomial time algorithm to re-rank the execution order of CARs by rules’ priority to reduce the influence of the rule dependency problem. Consequently, the performance (the classification accuracy and recall rate) of the associative classification algorithm can be improved.
論文目次 目錄
第1章 緒論 1
1.1 研究動機與目的 1
1.2 研究架構 3
第2章 相關文獻與研究探討 5
2.1 Apriori演算法 5
2.2 關聯式分類 (Associative Classification) 9
2.2.1 Lazy 12
2.2.2 CBA 15
2.2.3 CMAR 19
2.2.4 CPAR與PRM 20
2.3 文件分類(Document Classification Or Text Categorization) 23
2.4 評估方式 27
第3章 問題描述 29
第4章 研究方法 33
4.1 The Multi Levels Rule Priority Algorithm 33
4.2 The Multi Levels Class Priority Algorithm 39
第5章 實驗結果 46
5.1 實驗結果(1)-使用Reuters-21578 46
5.1.1 資料來源 46
5.1.2 The Multi Levels Rule Priority Algorithm實驗結果 53
5.1.3 The Multi Levels Class Priority Algorithm實驗結果 57
5.2 實驗結果(2)-使用UCI資料庫 58
5.2.1 資料來源 58
5.2.2 The Multi Levels Rule Priority Algorithm實驗結果 60
5.2.3 The Multi Levels Class Priority Algorithm實驗結果 65
5.3 綜合比較 68
第6章 結論 75
Bibliography 76
VITA 83
附錄A 84
附錄B 91
附錄C 98
附錄D 104
附錄E 110
附錄F 116

圖目錄
圖 2-1 產生候選項目 7
圖 2-2 計算候選項目次數 7
圖 2-3 Apriori演算法過程 9
圖 2-4 database coverage 演算法 11
圖 2-5 L3 規則修剪演算法 15
圖 2-6 CBA-RG 演算法 16
圖 2-7 CBA-CB Naïve(called M1) Algorithm 17
圖 2-8 Selecting rules based on database coverage 20
圖 2-9 PRM演算法 22
圖 2-10 文件分類流程圖 24
圖 2-11 特徵詞刪除成效比較 25
圖 2-12 關聯式分類器分類流程示意圖 26
圖 2-13 文件數量分佈表 27
圖 4-1 MLRP 演算法流程圖 34
圖 4-2 MLRP演算法 36
圖 4-3 Rerank_Priority對最高階層的規則重新排序規則演算法 38
圖 4-4 選擇規則並回傳Improve value最大值 39
圖 4-5 MLCP 演算法流程圖 41
圖 4-6 MLCP演算法 42
圖 4-7 Rerank_Priority對最高階層的規則重新排序規則演算法 44
圖 4-8 選擇規則並回傳Improve value最大值 45
圖 5-1 Reuters文件標籤 46
圖 5-2 Reuters文件範例 48
圖 5-3 L3與L3 with MLRP五次實驗正確率比較 56
圖 5-4 L3與L3 with MLCP五次實驗正確率比較 58
圖 5-5 UCI_Balance資料庫五次MLRP實驗正確率 64
圖 5-6 UCI_Vehicle資料庫五次MLRP實驗正確率 65
圖 5-7 UCI_Balance資料庫五次MLCP實驗正確率 67
圖 5-8 UCI_Vehicle資料庫五次MLCP實驗正確率 68
圖 5-9 Reuters資料庫L3 with MLRP與L3 with MLCP五次實驗正確率比較 69
圖 5-10 Balance資料庫L3 with MLRP與L3 with MLCP五次實驗正確率比較 69
圖 5-11 Vehicle資料庫L3 with MLRP與L3 with MLCP五次實驗正確率比較 69
圖 5-12 Reuters資料庫L3 with MLRP與L3 with MLCP五次實驗執行時間比較 70
圖 5-13 Balance資料庫L3 with MLRP與L3 with MLCP五次實驗執行時間比較 70
圖 5-14 Vehicle資料庫L3 with MLRP與L3 with MLCP五次實驗執行時間比較 71
圖 5-15 Reuters資料庫L3 with MLRP與L3 with MLCP準確率比較圖 72
圖 5-16 Balance資料庫L3 with MLRP與L3 with MLCP準確率比較圖 72
圖 5-17 Vehicle資料庫L3 with MLRP與L3 with MLCP準確率比較圖 72
圖 5-18 Reuters資料庫L3 with MLRP與L3 with MLCP執行時間比較圖 73
圖 5-19 Balance資料庫L3 with MLRP與L3 with MLCP執行時間比較圖 74
圖 5-20 Vehicle資料庫L3 with MLRP與L3 with MLCP執行時間比較圖 74

表目錄
表 2-1 關聯式規則搜索與關聯式分類差異表 10
表 3-1 範例規則1 30
表 3-2 範例規則2 31
表 3-3 範例規則3 31
表 3-4 範例規則4 31
表 5-1 不同分類的文件數 49
表 5-2 訓練及測試文件數 49
表 5-3 stop word list的部分範例 51
表 5-4 DB2 Intelligent Miner關聯式規則範例 52
表 5-5 Classification results from using L3 without Rule Priority(Reuters-21578) 54
表 5-6 Classification results from using L3 with MLRP Algorithm(Reuters-21578) 55
表 5-7 Classification results from using L3 with MLCP Algorithm(Reuters-21578) 57
表 5-8 Balance資料庫範例 59
表 5-9 Vehicle資料庫範例 59
表 5-10 UCI_Balance資料庫訓練及測試資料筆數 60
表 5-11 UCI_Vehicle資料庫訓練及測試資料筆數 60
表 5-12 Classification results from using L3 without Rule Priority(Balance) 61
表 5-13 Classification results from using L3 with MLRP Algorithm(Balance) 61
表 5-14 Classification results from using L3 without Rule Priority(Vehicle) 62
表 5-15 Classification results from using L3 with MLRP Algorithm(Vehicle) 63
表 5-16 Classification results from using L3 with MLCP Algorithm(Balance) 65
表 5-17 Classification results from using L3 with MLCP Algorithm(Vehicle) 66
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