臺灣博碩士論文加值系統

在資料探勘的領域之中，資料分類往往是最常被討論的議題之一，已有許多研究方法透過建立數學模型用以區分未分類資料，而支持向量機(Support vector machine, SVM)則是近年以統計學習理論所延伸發展出來的機器學習方法，其已被廣泛地被應用於統計分類及回歸分析中。然而，利用支持向量機處理各種獨特的分類問題時，目前並無任何準則、規範供使用者參考以選擇具有較佳分類準確率之參數設定，通常使用者需要自行不斷測試各種參數組合或者將支持向量機結合一些具學習能力之演算法，以求得具有較佳準確率之參數組，進而將此參數組套入支持向量機以對未知資料進行分類。故本研究乃提出以簡化粒子演算法為基礎之支持向量機分類法(SSO-SVM)，本方法論乃以簡化粒子演算法(Simplified swarm optimization, SSO)作為產生支持向量機所需參數之基礎，以較簡單快速之方式產生具有較佳準確度之參數組合；而支持向量機之分類準確度則回饋予簡化粒子演算法，作為各參數組合所對應之適應度，以作為產生新參數組合之基礎。此外，本研究將利用UCI資料庫之多筆標竿問題進行實驗，以比較本方法論與其他相似之分類演算法於資料分類準確度上之優劣。

In the field of data mining, classification is one of the most discussed issues that generating a generalized known structure to apply to new data. Recently, support vector machine (SVM) has been introduced for analyzing data and recognizing patterns. It’s a useful technique for data classification and regression analysis. However, while using SVM dealing with each unique classification problem; it is not known beforehand which parameter combination is the best for a given problem. Users often need to do random self-test or apply other algorithm to find an acceptable solution. In this study, we proposed a support vector machine classification combined with swarm intelligence algorithm, called Support Vector Machine based on Simplified Swarm Optimization (SSO-SVM). The simplified swarm optimization (SSO) is an emerging population-based stochastic optimization method, which belongs to both categories of swarm intelligence and evolutionary computation. In this paper, simplified swarm optimization (SSO) is used to implement a parameter combination selection, and support vector machine (SVM) serve as a fitness function of SSO for classification problem. The result indicates that the proposed SSO-SVM has better performance and more efficient than other method listed in this paper.

中文摘要 I
Abstract II
致謝 III
Table of Contents IV
List of Tables V
List of Figures VI
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 3
1.3 Overview of this thesis 5
Chapter 2 Literature review 6
2.1 Survey of SVM 6
2.2 Survey of PSO 13
2.3 Survey of GA 17
2.4 Survey of SSO 21
Chapter 3 Research methodology 25
3.1 The individual framework of SSO 25
3.2 The individual framework of SVM 26
Chapter 4 Experiment results 29
4.1 Benchmark datasets 29
4.2 Experiment parameter settings 30
4.3 Experiment results 31
Chapter 5 Conclusion 32
Reference 33

[1] B. E. Boser, I.M. Guyon and V. N. Vapnik, A training algorithm for optimal margin classifiers, In D. Haussler, editor, 5th Annual ACM Workshop on COLT, pages 144-152, Pittsburgh, PA, 1992. ACM Press.
[2] C. Cortes and V. Vapnik. Support-vector network. Machine Learning, 20:273-297, 1995.
[3] J. Li, S. Huang, R. He and K. Qian, Image Classification Based on Fuzzy Support Vector Machine, 2008 International Symposium on Computational Intelligence and Design
[4] B. Zhao and Y.Qi, Image Classification with Ant Colony Based Support Vector Machine, Control Conference (CCC), 2011 30th Chinese
[5] D. Zhao, W.Zou and G. Sun, A Fast Image Classification Algorithm using Support Vector Machine, 2010 2nd International Conference on Computer Technology and Development (ICCTD)
[6] X. Jia, B. Zhao and Z. Chen, Genetic Algorithm Optimization Based on Support Vector Machine Image Interpolation, 2011 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC)
[7] W. Zhuo and L. Chu, The Algorithm of Text Classification Based on Rough Set and Support Vector Machine, 2010 2nd International Conference on Future Computer and Communication
[8] H. Pan, Y. Duan and L. Tan, Application for Web Text Categorization Based on Support Vector Machine, 2009 International Forum on Computer Science-Technology and Applications
[9] M. Zhou and Q. Wang, The On-line Electronic Commerce Forecast Based on Least Square Support Vector Machine, 2009 Second International Conference on Information and Computing Science
[10] T. Wang, H. Wang and H. Xie, Research on Networked Synchronization Control Model by the Combination of Genetic Algorithm and Support Vector Machine, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE)
[11] J. Wang and J. Li, Research of protein structure classification based on rough set and support vector machine, Apperceiving Computing and Intelligence Analysis, 2009. ICACIA 2009. International Conference on
[12] Chamasemani, F. F. and Singh, Y.P., Multi-class Support Vector Machine (SVM) classifiers – An Application in Hypothyroid detection and Classification, 2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications
[13] Kiran, C.G., Prabhu, L.V., Abdu, R.V. and Rajeev, K, Traffic Sign Detection and Pattern Recognition using Support Vector Machine, ICAPR '09. Seventh International Conference on Advances in Pattern Recognition
[14] R.Shantha S. K., Sugumar, D. and Sadasivam V., International Conference on Conference on Computational Intelligence and Multimedia Applications, 2007.
[15] J. Kennedy, R. Eberhart, Particle swarm optimization, in Proceedings of IEEE International Conference on Neural Networks, vol. 4, 1995, pp. 1942–1948.
[16] Z. Wang, Q. Zhang and D. Zhang, A PSO-Based Web Document Classification Algorithm, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing.
[17] Z. Wang and X. Sun, Document Classification Algorithm Based on NPE and PSO, 2009. EBISS '09. International Conference on E-Business and Information System Security.
[18] D. Tan, Application of Chaotic Particle Swarm Optimization Algorithm in Chinese Documents Classification, 2010 IEEE International Conference on Granular Computing.
[19] Satapathy, S.C., Murthy, J.V.R., P. Rao, B.N.V.S.S. and P. Reddy, P.V.G.D, A Comparative Analysis of Unsupervised K-means, PSO and Self Organizing PSO for Image Clustering, 2007 International Conference on Computational Intelligence and Multimedia Applications.
[20] Ahmadyfard, A. and Modares, H., Combining PSO and k-means to Enhance Data Clustering, 2008 International Symposium on Telecommunications
[21] H. Jiang, F. Tang and X. Zhang, Liver Cancer Identification Based on PSO-SVM Model, 2010 11th Int. Conf. Control, Automation, Robotics and Vision
[22] H. Zhang and H. Mao, Feature Selection for the Stored-grain Insects Based on PSO and SVM, Second International Workshop on Knowledge Discovery and Data Mining
[23] C. Hsu, C. Yang, Y. Chen and M. Tsai, A PSO-SVM Lips Recognition Method Based on Active Basis Model, 2010 Fourth International Conference on Genetic and Evolutionary Computing
[24] J. Gao, J. Peng and Z. Li, Application of Improved PSO-SVM Approach in Image Classification, 2010 Symposium on Photonics and Optoelectronic (SOPO).
[25] Y. Zhang, X. Xie and T. Cheng, Application of PSO and SVM in Image Classification, 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT).
[26] S. Ding and S. Li, PSO Parameters Optimization Based Support Vector Machines for Hyperspectral Classification, The 1st International Conference on Information Science and Engineering (ICISE2009)
[27] J.H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, 1975
[28] M. Chen and Z. Yao, Classification Techniques of Neural Networks Using Improved Genetic Algorithms, 2008 Second International Conference on Genetic and Evolutionary Computing
[29] X. Li, C. Xin and L. Yang, Study of Data Ming Classification based on Genetic Algorithm, 20IO 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE)
[30] Patel, R., Raghuwanshi, M.M. and Jaiswal, A.N., Modifying Genetic Algorithm with Species and Sexual Selection by using K-means Algorithm, 2009 WEE International Advance Computing Conference (LCC 2009)
[31] C. Cao, B. Zhang, L. Wang and W. Li, The Application of the Genetic algorithm-Ant algorithm in the Geometric Constraint Satisfaction Guidelines, ICCI 2006. 5th IEEE International Conference on Cognitive Informatics.
[32] W.-C. Yeh. Study on quickest path networks with dependent components and apply to RAP. Report. NSC 97-2221-E-007-099-MY3. 2008/08/01–2011/07/31
[33] W.-C. Yeh, Novel swarm optimization for mining classification rules on thyroid gland data, Inform. Sci.(2012), doi:10.1016/j.ins.2012.02.009
[34] W.-C. Yeh, Simplified swarm optimization in disassembly sequencing problems with learning effects, Computers &; Operations Research, Volume 39, Issue 9, September 2012, Pages 2168-2177
[35] C.-C. Chang and C.-J. Lin. LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011.