在使用(Associative Classification, AC)做分類時，通常會將無法利用AC分類的資料，直接歸類到一個預先設定的類別，以避免資無法被分類的問題。但在使用AC建立分類器時，最容易遇到規則建立後門檻值設定的問題，定得太高會將很多可能有用的規則刪除而造成許多test cases不能分類，而太低又容易產生分類錯誤，這些情形都會影響到分類準確性。為了解決上述問題，提升分類結果的準確度，我們提出同時使用兩種不同分類器的概念，依據分類器特性，在不同階段做不同的事。本論文將利用KNN或貝氏分類器對文件做初步分類，然後利用所得之分類結果設定各種門檻值來篩選出滿足門檻值條件的關聯式分類法則(Associative Classification Rules, ACR)，由於這些ACR之準確度皆高於初步分類的結果，我們可利用此特性篩選出ACR來進一步改善分類的結果。針對ACR不能分類的文件，則以KNN或貝氏分類器計算詞彙權重來分類，因此可減少規則產生的時間及數量進而加快分類速度，所以結合不同的分類器的優點則可有效提升文件分類的效能。經由實驗證明，使用本論文提出之結合兩種不同分類器的確可獲得比單一分類器更好的分類效能。

In recent years many wireless broadband networking technologies were brought up and discussed. IEEE 802.16j is one of the most impressive one with its MMR network structure. Through the mechanism of Relay Station, high-cost base station could be substituted to give a broader network coverage and bigger bandwidth. Therefore the choice of location of these relay stations has become a topic that is worth discussing. This dissertation will start from the present IEEE 802.16 network, to figure out a relay station placement mechanism. Inside the acceptable range of the base station, find the best and most efficient sub-area for relay station by considering the differences in data flow and attributed bandwidth. The findings of this research will be verified through a series of experiments, and to finally conclude with the most suitable rule for the placement of relay stations

目錄

Table of Contents	III
List of Figures	V
List of Tables	VI

第一章  緒論	1
1.1.	研究的動機與目的	1
1.2.	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 Rule Generation	6
2.1.2 Ranking	8
2.1.3  Pruning	9
2.1.4 關聯式分類器	11
2.2 文件分類	18
2.2.1 文件分類流程	18
2.2.2 特徵萃取	20
2.3其他分類器演算法	23
2.3.1　KNN最近鄰居法則[19]	23
2.3.2　貝氏分類法Naïve Bayesian Classifier [21]	27
2.4　評估值：	29
第三章 研究方法與執行步驟	31
3.1　研究方法	31
3.1.1  Lazy AC分類器及多重分類器	32
3.2　分類系統流程	34
3.3　KNN分類器	36
3.3.1空間向量的建立	36
3.3.2  KKN分類實作	37
3.4 貝式分類器	39
3.5　初次分類	39
3.6 門檻值設定	40
3.7　執行策略	40
第四章	實驗結果分析	44
4.1資料來源	44
4.2資料預處理結果	46
4.3  Lazy分類結果	47
4.4 KNN及貝式之分類結果	49
4.5 門檻值設定	51
4.6 Precision-based thresholds	53
4.7 F1-based thresholds	55
第五章	結論與未來展望	57
5.1 結論	57
5.2 未來展望	57
文獻參考	58
附錄－英文論文	61


圖目錄 
圖2.1.2-1 Rule ranking method presented in Thabtah (2006)	9
圖2.1.4-5 L3演算法	15
圖2.1.4-6 關聯式分類器分類流程示意圖	16
圖2.2-1 文件分類之系統流程	20
圖2.3.1-1 KNN 分類方法示意圖	26
圖2.4-1 文件數量分佈表	29
圖3.2-1 分類流程圖	35


表目錄 
表2.1-1 關連式比較圖	5
表2.1.3-1 Rruning演算法	10
表4.1-1 由各系所選取出的文章數	44
表4.1-2 文件描述的格式	45
表4.2-1 文件經過斷詞以後的結果	46
表4.2-2 TFIDF所得的特徵詞各項數據統計與權重	47
表4.3-1 採用Lazy法之結果	48
表4.4-1 KNN 分類結果	49
表4.4-2　貝氏分類器分類 準確率及F1值	50
表4.5-1　單一門檻值	51
表4.5-2　多重門檻值	52
表4.6-１　以KNN及貝式的準確率為門檻值之AC分類結果	53
表4.7-１　以KNN及貝式的F1為門檻值之AC分類結果	55
表4.8-1 最佳實驗結果比較	56

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