研究生: |
韓歆儀 Han, Hsin-yi |
---|---|
論文名稱: |
應用兩階段分類法提昇SVM法之分類準確率 Applying the Two-Stage Classification to Improve the SVM Classification Accuracy |
指導教授: |
利德江
Li, Der-Chang |
學位類別: |
碩士 Master |
系所名稱: |
管理學院 - 工業與資訊管理學系 Department of Industrial and Information Management |
論文出版年: | 2004 |
畢業學年度: | 92 |
語文別: | 中文 |
論文頁數: | 76 |
中文關鍵詞: | 分類 、支撐向量機法 、決策樹 |
外文關鍵詞: | Decision Tree, SVM, Classification |
相關次數: | 點閱:105 下載:7 |
分享至: |
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分類問題是目前常被討論的主題之一,隨著資料的複雜度與資料量的增加,越來越多以統計學習架構為基礎的方法不斷延伸,支撐向量機法(Support Vector Machines)就是其中一種學習機器形式。
支撐向量機法是一個較新的分類方法,其應用於分類的領域也迅速增加,然而在資料複雜度高的情況下此模式的分類準確率仍會降低,因此如何在資料量與複雜度增加的情況下,保持或提升資料分類的準確度,是本研究的方向。本研究從評估資料複雜度之角度,即分析資料在空間中分部的散亂集中程度,判斷該資料是否具備兩階段分類之需要,若是,則以決策樹(Decision Tree)進行第一階段之步驟達到簡化資料的目的,再由支撐向量機法法進行第二階段分類。本研究最後透過驗證本兩階段之分類法確可提升支撐向量機法對複雜度較高的資料其分類的準確度。
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[中文部份]
王麒瑋, ”支向機核心函數適用指標之建立”, 國立成功大學工業管理科學研究所碩士論文, 2003年6月.
王景南, “多類支向機之研究”, 元智大學資訊管理研究所碩士論文, 2002年6月.
江其峰, ”支持向量機的特性篩選方法”, 東海大學數學研究所碩士論文, 2002年6月.
方振維, ”利用類神經網路技術提升決策樹效能之研究”, 銘傳大學資訊管理研究所碩士論文, 2002年6月.
周文輝, “醫院分類: An SVM Approach”, 國立東華大學應用數學研究所碩士論文, 2002年7月.
胡毓志, ”不完整資料學習演算法使用支援向量機器”, 國立交通大學資訊科學研究所碩士論文, 2002年6月.
蔡家昌, “應用決策樹歸納法探討台灣行動電話市場區隔”, 國立台北大學統計研究所碩士論文, 2002年6月.
Michael J.A. Berry, Gordon S. Linoff, 彭正文譯,《Data Mining 資料採礦─顧客關係管理暨電子行銷之應用》, 數博網資訊股份有限公司, 初版, 2001年1月, 26-50.
[英文部份]
Abe, S., “Analysis of Support Vector Machines”, Neural Networks for Signal Processing, Proceeding of the 12th IEEE Workshop on, Sept, 2002, pp89-98.
Ben-ur, A., Horn D., Siegelwann, H.T., Vapnik, V., “A Support Vector Clustering Method”, Proceedings of 15th International Conference on Pattern Recognition, Vol.2, Sept, 2000, pp724-727.
Bennett, K.P., Wu, S., Auslender, L., “On Support Vector Decision Trees for Database Marking”, IEEE transactions on Neural Network, Vol.2, July, 1999, pp904-909.
Bennett, K.P., Blue, J.A., “A Support Machine Approach to Decision Trees”, IEEE transactions on Neural Networks, Vol.3, May, 1998, pp2396-2401.
Ben-Hur A., Horn D., Siegelman H.T., Vapinik V., “Support vector clustering”, Journal of Machine Learning Research, Vol.2, 2001, pp125-137.
Breiman L., Friedman J.H., Olshen R.A., Stone C.J., “Classification and Regression trees”, Belmont, Calif:Wadsworth, 1984.
Burbudge R., Trotter M., Buxton B., Holden S., “Drug design by machine learning:Support vector machine for pharmaceutical data analysis”, Computers and Chemistry, May, 2001, p5-14.
Chih-Wei Hsu, Chih-Jen Lin, “A comparison of methods for multiclass support vector machines, IEEE transactions on Neural Networks, Vol.13, March, 2002.
Cortes, C., Vapnik, V., “Support-vector networks,” Machine Learning, 20, 273-297, 1995.
Duan Kaibo, Sathiya Keerthi S., Neow Poo Aun, “Evaluation of simple performance measures for tuning SVM hyperparameters”, Neurocomputering, March, 2003, p41-59.
Francis E.H. Tay, Lijuan Cao, “Application of support vector machines in financial time series forecasting”, The international Journal of Managament Science Omega, March, 2001, pp309-317.
Glenn Fung, Olvi L. Mangasarian, “Proximal Support Vector Machine Classifiers”, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 2001, pp77-86.
Glenn F., Olvi L. Mangasarian, “Data Selection for Support Vector Machine Classifiers”, Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, August, 2001, pp77-86.
Harris Drucker, S Donghui Wu, Vladimir N. Vapnik, “Support Vector Machines for Spam Categtrization”, IEEE Transactions on Neural Networks, Vol.10, Sept, 1999, pp1048-1054.
Ian H. Witten, Eibe Frank, “Data Mining Practical Machine Learning Tools an Techniques with Java Implementations”, Morgan Kaufmann Pulishers, 2000, pp127-128,157-183,188-193.
Kan Li, Yu-Shu Lin, “Choosing of battle position through training support vector machines”, Machine Learning and Cybernetics, Proceedings International Conference on, Vol.1, Nov. 2002, pp18-20.
Lijuan Cao, “Support vector machines exports for time series forecasting”, Neurocomputing, 2003, pp321-339.
Monica Rogati, Yiming Yang, “High-performing Feature Selection for Text Classification”, Conference on Information and Knowledge Management, Proceedings of the eleventh international conference on Information and knowledge management, 2002, pp659-661.
Nello Cristinanini, John Shawe Taylor, “An Introduction to Support Vector Machines and other kernel based learning methods”, The press syndicate of the University of Cambridge, 1thed, 2002, pp9-11, 94-97.
Rohlf, F.J., “Methods of Comparing Classifications,” Annual Review of Ecology and Systematics, Vol.5, 1974, pp.101-113.
Ross Quinlan J., “C4.5:Progeams for Machine Learning”, Morgan Kaufmann Publishers, Pat Langley, San Mateo, CA, Series Editor, October 1993.
Ross Quinlan J., “Induction of Decision Trees”, Machine Learning, 1986.
Sohwenker F., Kestler HA, “Analysis of Support Vectors helps to Identify Borderline Patients in Classification Studies”, Computers in Cardiology, 2002, pp305-308.
Sun-Bae Cho, Hong-Hee Won, “Machine Learning in DNA Microarray Analysis for Cancer Classification”, Asia-Pacific Bioinformatics Conference in Information Technology, 2003.
Tom M. Mitchell, “Machine Learning,” McGraw-Hill International Editions, 1thed, 1997, pp52-71.
Takahash, F., Abe, S., “Decision-tree-based multi-class support vector machine”, Neural Information Processing, Proceeding the 9th International Conference on, Nov, 2002, pp1418-1422.
Vapnik, V., “ Statistical Learning Theory,” Wiley, NY, 1998.
Vishwanathan, S.C.M., Narasimha Murty, M., “SSVM:A Simple SVM Algorithm”, Neural Network, Proceedings of the 2002 International Joint Conference on, Vol.3, May, 2002, pp12-17.
Weida Zhou, Li Zhang, Licheng Jiao, “Linear programming support vector machines”, The Journal of the Pattern Recognition society, October, 2002, pp2927-2936.
Yong, M. and Xiaoqing, D., “Face Detection Based on Cost-Sensitive Support Vector Machines,” State Key Laboratory of Intelligent Technology and System Dept. of Electtronic Engineering, Tsinghua University, Beijing 100084, P. R. Chian.