基於整合特徵詞擷取方法的文件分類系統｜國立清華大學博碩士論文庫

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研究生：	陳奕廷 Chen, Yi-Ting
論文名稱：	基於整合特徵詞擷取方法的文件分類系統 A Text Classification System Based On Integrated Feature Selection Approach
指導教授：	張適宇 Chang, Shih-Yu
口試委員:	徐正炘許慶賢
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 通訊工程研究所 Communications Engineering
論文出版年：	2012
畢業學年度：	100
語文別：	英文
論文頁數：	44
中文關鍵詞：	特徵詞擷取、文件分類、互消息
相關次數：	點閱：1 下載：0
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在文件分類中，特徵擷取技術是一項極為重要的過程，因為該技術與分類器處理的正確性有著相當大的關係。當原有的文集非常龐大時，基於處理時間的考量，從原有的文集中選擇適當的特徵詞，對於減少處理的時間將更有幫助。我們經由對文件分類中特徵擷取技術的研究，發現先前的研究中，很少有研究能夠有效地結合特徵擷取技術，通常都是使用單一的特徵擷取技術，以計算特徵詞的權重。在本研究中，我們提出一套整合的特徵擷取方法，是結合了知名的mutual information (MI)和term frequency–inverse document frequency (TF-IDF)方法，以及改進的向量空間模型，為的是解決特徵空間維度過高的問題，以及改進文件分類的效能。此方法是先計算每一個特徵詞的MI值，以便從訓練資料集中區分出較不重要的詞，接著將每一個詞的MI值乘上自己的TF-IDF值，以進一步的加強較重要的詞的權重。本研究是應用於中文文件上，其中文件集是從台灣奇摩新聞網站中得到。經由實驗結果證實，我們提出的整合特徵擷取方法，其效能優於傳統的TF-IDF和MI兩項方法。

Introduction and Literature Review 1
1 Background . . . . . . . . . . . . . . . . . . . . .  1
2 Research Motivation . . . . . . . . . . . . . . . . . 3
3 Organization of Thesis . . . . . . . . . . . . . . .  4
Previous Work 6
1 Vector Space Model . . . . . . . . . . . . . . . . .  7
2 Feature Selection Methods . . . . . . . . . . . . . . 7
2.1 Mutual Information . . . . . . . . . . . . . . . .  7
2.2 Term frequency-inverse document frequency . . . . . 8
3 Text Classier . . . . . . . . . . . . . . . . . . . . 9
3.1 Decision Tree . . . . . . . . . . . . . . . . . . . 9
3.2 K-Nearest Neighbors . . . . . . . . . . . . . . . .10
3.3 Nave Bayesian . . . . . . . . . . . . . . . . . . .11
4 Support Vector Machine . . . . . . . . . . . . . . . 12
The Proposed Approach 15
1 Architecture for a Text Classication System . . . . .15
2 TF calculation and MI threshold . . . . . . . . . . .17
3 The Improved Feature Selection Method . . . . . . . .18
4 Evaluate Weight with VSM . . . . . . . . . . . . . . 19
5 Time Complexity Analysis . . . . . . . . . . . . . . 19
6 The steps of the proposed method . . . . . . . . . . 20
6.1 Documents collection . . . . . . . . . . . . . . 20
6.2 Word segmentation . . . . . . . . . . . . . . . .21
6.3 Stop words removal . . . . . . . . . . . . . . . 21
6.4 Evaluate of the value of TF and MI and IDF . . . 22
6.5 Constructing a Vector Space Model . . . . . . . .23
6.6 Training SVM classier with VSM of training documents 24
Experiments Result 25
1 Corpus . . . . . . . . . . . . . . . . . . . . . . . 25
2 Evaluation Metrics . . . . . . . . . . . . . . . . . 26
3 Results . . . . . . . . . . . . . . . . . . . . . . .27
3.1 Experiment 1 . . . . . . . . . . . . . . . . . . . 27
3.2 Experiment 2 . . . . . . . . . . . . . . . . . . . 30
3.3 Experiment 3 . . . . . . . . . . . . . . . . . . . 35
Conclusions 40
  Reference . . . . . . . . . . . . . . . . . . . . . . . .42
                                

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