電子郵件是現代人最常使用的通訊工具，然而許多業者為了強迫促銷，便大量的傳送電子郵件以達到廣告宣傳的效果，造成了使用者的困擾。近來，許多學者提出利用資料探勘、人工智慧等技術來幫忙過濾電子郵件，以還給使用者一個乾淨清新的收信環境。
但是目前的方法尚有不足之處。例如分類器的建置、演進式學習能力以及客製化郵件處理等議題仍待探究。本研究的目的即在於建構一個具有演進式學習功能的分類器，能從錯誤中逐漸學習改正；並提出一個客製化郵件處理流程，針對每一個使用者之喜好而客製化處理其郵件，並且以所建構的分類器偵測使用者喜好改變，加以學習適應，以達到客製化電子郵件處理的目的。
本研究提出兩個實驗來驗證所建構的分類器效能，其實驗結果有相當高的準確率（Accuracy）和準確度(precision)，最後以一個實際個案來說明如何應用所提的客製化郵件處理流程，並驗證所建構的分類器能偵測使用者喜好改變，並加以學習適應。這些實驗結果驗證了本研究所提方法的適用性。

The electronic mail has become one of the most popular communication channels in the modern world. Due to its convenience and low cost, however, many business salesmen utilize this channel to promote their products by distributing e-mails to people as far as they can reach, which causes troubles to irrelevant e-mail receivers. As a result, many a research has been devoted to filtering irrelevant e-mails based on data mining techniques to alleviate users’ mental loadings in processing e-mails they receive.
Nevertheless, current approaches have their own drawbacks. Issues on what appropriate classifies to construct, how to endow such classifiers with the adaptive learning ability, and how to customize the e-mail management process for each user are still under investigation. The objective of this research is therefore to construct an e-mail classifier with learning ability to self-correct from erroneous outcomes. Furthermore, we propose a customized e-mail management process that can handle users’ e-mails based on their own preferences. Ultimately, it can adapt itself to the changes of users’ preferences when handling their e-mails.
Several experiments are conducted to verify the performance of the constructed classifier. The results show that our proposed classifier possesses high accuracy and high precision with outstanding adaptive learning ability. We also illustrate a real application of the customized e-mail management process. It shows that our approach can detect the changes of users’ preferences and learn to follow the changes. The feasibility of employing our approach to constructing e-mail classifiers is thus justified.

第一章 緒論 1
1.1 研究背景 1
1.2 研究目的 2
1.3 論文架構 3
第二章 文獻探討 4
2.1 文字探勘(Text Mining) 4
2.2學習式分類方法 5
2.2.1 貝氏分類器(Naïve Bayes) 6
2.2.2最近鄰居法(KNN) 6
2.2.3 決策樹分類法(Decision Tree) 7
2.2.4 支援向量機(SVM) 8
2.3 電子郵件過濾之相關研究 10
2.3.1 過濾電子郵件的方法 10
2.3.2 過濾電子郵件的系統 11
2.3.3 智慧型郵件代理人 12
第三章 電子郵件過濾方法 15
3.1 電子郵件分類器 15
3.1.1分類器的建構 15
3.1.2 分類器的選擇 17
3.2 分類器演進式學習 19
3.3 客製化郵件處理程序 22
3.3.1 伺服器端代理人 22
3.3.2 客戶端代理人 23
3.3.3 演進式學習之代理人互動 25
3.3.4 起始分類器之訓練 25
第四章 實驗與結果 27
4.1實驗設計 27
4.2 實驗一 29
4.3 實驗二 32
4.4 客製化郵件處理之實例 36
第五章 結論 40
參考文獻 42

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