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研究生:謝易修
研究生(外文):Yi-Hsiu Hsieh
論文名稱:基於內分泌粒子群演算法及人造蜂群演算法之分類模型選取
論文名稱(外文):A hybrid optimization algorithm based on Endocrine Particle Swarm and Artificial Bee Algorithm for classification model selection
指導教授:林冠成林冠成引用關係
口試委員:黃一泓洪啟舜
口試日期:2015-07-20
學位類別:碩士
校院名稱:國立中興大學
系所名稱:資訊管理學系所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:46
中文關鍵詞:分類特徵選取混合式進化演算法粒子群最佳化演算法人造蜂群演算法支援向量機群體智慧
外文關鍵詞:ClassificationFeature selectionHybrid evolutionary algorithmParticle swarm optimizationArtificial bee colonySupport Vector Machine
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數據資料的分析和分類在當今的科學研究當中是一個很重要的議題,在巨大資料中選出一些適合的特徵能夠幫助人們快速且有效率的把資料分門別類。而特徵選取可是被視為是一個特徵子集合的選擇問題,同時也是一個組合最佳化問題。
進化式演算法中在過程中使用了隨機搜尋的方法來解決最佳化問題,同時也被證實在多種應用上都有相當的成效。本篇研究提出的混合形式進化式演算法是基於內分泌粒子群演算法(Endocrine-Based Particle Swarm Optimization)及人造蜂群演算法(Artificial Bee Colony),同時結合支援向量機(Support Vector Machine)對資料集進行特徵選取與資料分類。
以UCI(University of California, Irvine)資料集進行分類結果顯示本研究提出的混合方法的搜尋精度優於內分泌粒子群與人造蜂群演算法,同時也能找出特徵數較少的特徵子集合。


The classification and analysis of data is an important issue in today''s research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems.
Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets.
The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features.

摘要 i
Abstract ii
目次 iii
圖片目次 v
表格目次 vi
第一章 緒論 1
1.1 研究背景與動機 1
1.2 論文架構 3
第二章 相關研究 4
2.1 支援向量機(Support Vector Machine, SVM) 4
2.2 群體智慧(Swarm Intelligence) 6
2.3 粒子群最佳化演算法(Particle Swarm Optimization, PSO) 6
2.3.1 初始化粒子群 6
2.3.2 更新粒子群個體最佳位置和全域最佳位置 7
2.3.3 更新粒子的速度、位置和參數 7
2.3.4 判斷達到終止條件 8
2.4 內分泌機制粒子群最佳化演算法(Endocrine-based Particle Swarm Optimization, EPSO) 10
2.5 人造蜂群演算法(Artificial Bee Colony, ABC) 11
2.5.1 初始化人造蜂群 11
2.5.2 工蜂階段(Employed bee phase) 12
2.5.3 觀察蜂階段(Onlooker bees phase) 13
2.5.4 偵查蜂階段(Scout bees phase) 14
2.5.5 判斷達到終止條件 15
2.6 混合式方法(Hybrid methods) 17
第三章 混合式內分泌粒子群及人造蜂群演算法 18
3.1 混合機制 18
3.2 混合型式內分泌機制粒子群及人造蜂群演算法流程 20
3.3 EPSOABC特徵選取結合支援向量機分類器 21
第四章 實驗結果 24
4.1 實驗架構與環境配備 24
4.2 演算法效能分析 27
4.2.1 特徵選取與未特徵選取比較 27
4.2.2 特徵選取和支援向量機參數搜尋效能分析 30
4.2.3 演算法分類正確率與時間之比較 32
4.3 資料集分析 39
第五章 結論與未來研究方向 43
參考文獻 44


[1]許聖華. (2012). 植基於改良式內分泌粒子群演算法之 支持向量機特徵選取與參數最佳化. 中興大學資訊管理學系所學位論文, 1-40.

[2]Piateski, G., & Frawley, W. (1991). Knowledge discovery in databases. MIT press.

[3]Raghupathi, W. (2010). Data mining in health care. Healthcare Informatics: Improving Efficiency and Productivity, 211-223.

[4]Mannila, H. (1996, June). Data mining: machine learning, statistics, and databases. In Scientific and Statistical Database Management, International Conference on (pp. 2-2). IEEE Computer Society

[5]Quinlan, J. R. (1987). Simplifying decision trees. International journal of man-machine studies, 27(3), 221-234.

[6]Wang, S. C. (2003). Artificial neural network. In Interdisciplinary Computing in Java Programming (pp. 81-100). Springer US.

[7]Peterson, L. E. (2009). K-nearest neighbor. Scholarpedia, 4(2), 1883.

[8]Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning,20(3), 273-297.

[9]Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2012). Foundations of machine learning. MIT press.

[10]Dash, M., & Liu, H. (1997). Feature selection for classification. Intelligent data analysis, 1(3), 131-156.

[11]Furey, T. S., Cristianini, N., Duffy, N., Bednarski, D. W., Schummer, M., & Haussler, D. (2000). Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics,16(10), 906-914.

[12]Livadas, C., Walsh, R., Lapsley, D., & Strayer, W. T. (2006, November). Usilng machine learning technliques to identify botnet traffic. In Local Computer Networks, Proceedings 2006 31st IEEE Conference on (pp. 967-974). IEEE.

[13]Shin, C., Doermann, D., & Rosenfeld, A. (2001). Classification of document pages using structure-based features. International Journal on Document Analysis and Recognition, 3(4), 232-247.

[14]Liu, H., & Motoda, H. (1998). Feature selection for knowledge discovery and data mining. Springer Science & Business Media.

[15]Holland, J. H. (1975). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. U Michigan Press.

[16]Kirkpatrick, S., & Vecchi, M. P. (1983). Optimization by simmulated annealing. science, 220(4598), 671-680.

[17]Vapnik, V. (2013). The nature of statistical learning theory. Springer Science & Business Media.

[18]Kennedy, J., and Eberhart, R. C. (1995). Particle swarm optimization. Proc. IEEE International Conference on Neural Networks (Perth, Australia), IEEE Service Center, Piscataway, NJ, pp. IV: 1942-1948.

[19]Vert, J. P., Tsuda, K., & Schölkopf, B. (2004). A primer on kernel methods.Kernel Methods in Computational Biology, 35-70.

[20]Chen, D. B., & Zhao, C. X. (2007). Particle swarm optimization based on endocrine regulation mechanism. Control Theory and Applications, 24(6), 126-134.

[21]Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization (Vol. 200). Technical report-tr06, Erciyes university, engineering faculty, computer engineering department

[22]Youssef, H., Sait, S. M., & Adiche, H. (2001). Evolutionary algorithms, simulated annealing and tabu search: a comparative study. Engineering Applications of Artificial Intelligence, 14(2), 167-181.

[23]Wang, X. (2009). Hybrid nature-inspired computation methods for optimization.

[24]Zhi-gang, W. (2012). Hybrid optimization algorithm based on particle swarm optimization and artificial bee colony algorithm. Science Technology and Engineering, 12(20), 4921-4925.

[25]Guo, Z. (2012). A hybrid optimization algorithm based on artificial bee colony and gravitational search algorithm. Int J Dig Content Tech Appl, 6(17), 620-626.

[26]Karaboga, D., & Akay, B. (2009). A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation, 214(1), 108-132.

[27]Liu, J., Zhang, X., & Ning, A. (2011). Hybrid optimization algorithm of PSO and ABC. Computer Engineering and Applications, 47(35).

[28]Altun, O., & Korkmaz, T. (2014). Particle Swarm Optimization–Artificial Bee Colony Chain (PSOABCC): A Hybrid Meteahuristic Algorithm. Scientific Cooperations International Workshops on Electrical and Computer Engineering Subfields

[29]Kong, X., Liu, S., & Wang, Z. (2013). A new hybrid artificial bee colony algorithm for global optimization. International Journal of Computer Science, (10), 1.

[30]Khanesar, M. A., Teshnehlab, M., & Shoorehdeli, M. A. (2007, June). A novel binary particle swarm optimization. In Control & Automation, 2007. MED''07. Mediterranean Conference on (pp. 1-6). IEEE.

[31]Hsu, C. W., Chang, C. C., & Lin, C. J. (2003). A practical guide to support vector classification.

[32]Blake, C., & Merz, C. J. (1998). UCI repository of machine learning databases [http://www. ics. uci. edu/∼ mlearn/MLRepository. html], Department of Information and Computer Science. University of California, Irvine, CA, 55.

[33]Salzberg, S. L. (1997). On comparing classifiers: Pitfalls to avoid and a recommended approach. Data mining and knowledge discovery, 1(3), 317-328.

[34]Shi, Y., & Eberhart, R. C. (1999). Empirical study of particle swarm optimization. In Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on (Vol. 3). IEEE.


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