在利用關聯式法則(Association Rule)做分類時，一般關聯式法則分類(Association Rule Classification)的信賴度門檻值設定，大多是依據經驗法則來設定單一且固定的信賴值(Confidence)為其門檻值(Threshold value)，所以在設定上較為主觀。同時為了提升分類的準確度，依經驗通常選取較高的信賴度當門檻值，但門檻值若設定太高時，則容易使得部分文件因缺乏規則無法判斷其歸屬的類別，而必須利用預設規則(default rule)將這些文件分類成預設類別；若將門檻值降低則可能造成文件分類錯誤而降低分類的效能。因此本論文將針對門檻值問題做相關探討。 
本論文針對信賴度門檻值問題，分兩部分來討論，一為採取靜態門檻值或動態門檻值；另外是採單一門檻值或多重門檻值。動態門檻值的概念是在每次分類後比較準確率是否有提升，決定是否向上修定原始的門檻值；而多重門檻值的概念則是可根據不同的類別，設定不同的門檻值。
實驗將依據不同的組合來設定門檻值，同時，希望能依據實驗的結果，找出如何能以客觀的方式來設定信賴度門檻值，並提升分類的效能。

While using association rule for classification, the experience for association classification rules setting is following single and fixed confidence threshold value, hence is comparatively subjective. In order to increase the accuracy of classification, usually choose higher confidence in accordance with experience, but if set the confidence too high, might cause a part of documentations failed to justify the attributes by lacking rules; if set the confidence too low, it may decrease the documentation classification efficiency.
This thesis focus on the threshold value discussion, which divides into two parts, one is static threshold value, though the training process is quicker and simpler, but during the classification procedure, the accuracy that originally already been improved could probably be influenced by follow-up lower confidence rule, namely this kind of confidence rule accuracy is low than the original threshold value setting, therefore may decrease the documentation classification efficiency, so that this thesis proposes the dynamic threshold value, to determine whether the threshold value is upward revision by after each classified whether comparative improved the accuracy or not, also propose in an objective way to set the confidence threshold value to improve the classification efficiency, this thesis proved by experiment the dynamic threshold value can obtain better classification efficiency than static threshold value.

第一章　緒論	1
1.1  前言	1
1.2  研究動機與目的	2
1.3  論文架構	4
第二章　相關文獻與研究探討	5
2.1 關聯式分類 (Associative Classification)	5
2.1.1 規則產生 (Rule Generation)	9
2.1.2 規則排序 (Ranking)	11
2.1.3 刪除規則 (Pruning)	12
2.1.4 關聯式分類器 (Association Rule Classifier)	15
2.1.5 多重分類器	16
2.2 TFIDF (Term Frequency Inverse Document Frequency)	18
2.3　貝氏分類器(Naïve Bayes Classifier)	19
2.4　評量值	21
第三章 研究方法	23
3.1 門檻值設定	23
3.2 靜態門檻值	24
3.3 動態門檻值	26
3.4 實驗步驟	27
3.5 執行策略	28
第四章　實驗結果	30
4.1資料來源	30
4.2 實驗結果	32
4.2.1 Precision-based分類結果	32
4.2.2 F1-based分類結果	34
4.3 實驗結果分析	36
第五章　結論與未來展望	37
5.1 結論	37
5.2 未來展望	38
文獻參考	39
英文論文	41

 
圖目錄
圖2-1　關聯式分類流程圖	6
圖2-2　CBA 排序法	11
圖2-3　Lazy 排序法	12
圖2-4　database coverage演算法	13
圖2-5　Lazy 演算法	14
圖3-1　靜態門檻值流程圖	24
圖3-2　靜態門檻值流程圖	26
 
表目錄
表2-1　關聯式規則搜索與關聯式分類器差異表	6
表2-2　利用AC單一分類器的實驗結果	16
表2-3　使用AC結合KNN分類法的多重分類器實驗結果	17
表2-4　文件數量分佈表	21
表4-1　由各系所選取出的文章數	30
表4-2　文件描述的格式	31
表 4-3　依準確率設定單一門檻值	32
表4-4　依準確率設定多重門檻值	32
表4-5　利用準確率為靜態及動態門檻值之分類準確率比較	33
表4-6　利用準確率為靜態及動態門檻值之分類文件正確數比較	34
表 4-7　依F1設定單一門檻值	34
表 4-8　依F1設定多重門檻值	34
表4-9　利用F1為靜態及動態門檻值之分類準確率比較	35
表4-10　利用F1為靜態及動態門檻值之分類文件正確數比較	35
表4-11　最佳實驗結果	36

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