本研究將機器學習方法套用到學生是否轉學或轉系上，利用南部某私立大學的資訊學院的資料進行研究，透過Decision Tree、SVM(Support Vector Machine)和Logistic Regression的基本框架下，來建立學生轉學或轉系之模型。透過模型所歸納出來的法則，提供給管理者擬定方針，針對欲要轉學或轉系的學生進行輔導或改善教學品質。本研究希望能夠降低轉學或是轉系的發生率。

Currently, universities in Taiwan are facing many management pressures on the insufficient enrollment due to the low birth rate. To make the school continuously operate and to enhance the competitiveness, the universities have to avoid the loss of students. In this study, we propose the student transfer problem meaning the loss of students who switch from one university to another. We use machine learning techniques to address the problem. We study different machine learning methods such as decision tree, support vector machine (SVM) and Logistic Regression, and use them to construct a student transfer prediction model. The prediction model can be used to predict whether a student will switch from one university to another. Our results show that the student transfer mode built by the decision tree has the best prediction capability. We also use our prediction model to infer several strategies that university administrative chiefs can use them to cope with the student transfer problem.

第一章 緒論 1
1.1研究背景與動機 1
1.2研究目的 1
1.3研究限制 3
1.4研究貢獻 3
1.5論文架構 3
第二章 文獻探討 5
2.1客戶流失 5
2.2機器學習 6
2.3應用監督式機器學習方法於客戶流失問題之研究探討 7
2.3.1 Decision tree 7
2.3.2 Alternating Decision Tree 8
2.3.3 Support Vector Machine 10
2.3.4 LibSVM 12
2.3.5 Logistic Regression 13
第三章 研究方法 15
3.1 研究架構 15
3.2資料描述 16
3.3資料前處理 16
3.4 評估方法 17
3.5 F-measure 18
3.6 Receiver Operating Characteristic cure 18
第四章 實證研究 21
4.1客戶流失套用到轉學生上 21
4.2客戶流失套用到轉系生上 46
第五章 結論與建議 57
參考文獻 59

一、中文文獻
吳碧娥，2014，“私校悲歌！105年大學將掀起少子化倒閉潮，北美智權報,，http://www.naipo.com/Portals/1/web_tw/Knowledge_Center/Editorial/publish-193.htm

二、英文文獻
Athanassopoulos, A.D. (2000), “Customer Satisfaction Cues To Support Market Segmentation and Explain Switching Behavior “, Journal of Business Research, Vol 47, 191-207
Bhattacharya, C. B. (1998), “When customers are members: Customer retention in paid member ship contexts, Journal of the Academy of Marketing Science, Vol 26, 31-44
Boser, B. E., Guyon, I. ＆ Vapnik, V. (1992), “A training algorithm for optimal margin Classifiers, ACM Press, 144–152
Bradley, A.P. (1997), “the use of the area under the curve in the evaluation of machine learning algorithms , Vol 30, 1145-1159
Chandar, M., Laha, A. ＆ Krishna, P. (2006), “Modeling churn behavior of bank customers using predictive data mining techniques, National Conference on Soft Computing Techniques for Engineering Applications, Vol 3, 24-26
Chang, C.C ＆ Lin, C.J. (2001), “LIBSVM: A library for support vector machines, Journal ACM Transactions on Intelligent Systems and Technology, Retrieved DEC 5, 2014, from http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf
Colgate, M., Stewart, K. ＆ Kinsella, R. (1996), “Customer defection: A study of the student market in Ireland, International Journal of Bank Marketing, Vol 14, 23-29.
Cortes, C. ＆ Vanpnik, V. (1995), “Support-Vector Networks, Machine Learning, Vol 20, 273-297
Elkhlifi, A. ＆ Faiz, R. (2007), “Machine Learning Approach for the Automatic Annotation of Events, AAAI Press, 362-367
Freund, Y. ＆ Mason, L. (1999), “The alternating decision tree learning algorithm, In Machine Learning：Proceedings of the Sixteenth International Conference, 124-133
Ganesh, J., Arnold, M.J. ＆ Reynolds, K.E. (2000), “Understanding the customer base of service providers: An examination of the differences between switchers and stayers, Journal of Marketing, Vol 64, 65-8
Gómez-Chova, L., Muñoz-Marí, J., Laparra, V., Malo-López, J. ＆ Camps-Vall G. (2011), “A Review of Kernel Methods in Remote Sensing Data Analysis, Augmented Vision and Reality Volume, Vol 3, 171-206
Hand, D.J. ＆ Till R.J. (2001), “A Simple Generalisation of the Area Under the ROC Cureve for Multiple Class Classification Problems, Vol 45, 171-186
Hanley, J.A. ＆ Mcneil B.J. (1982), “Themeaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology, Vol 143, 29-36
Huang, X.M. ＆ Hsu, P.H. (2014), “A discussion of training data for SVM classification – an example of hypersepctral image classification, TGIS, Vol 1,153-166
Jones, M.A., Mothersbaugh, D.L. ＆ Beatty, S.E. (2000), “Switching barriers and repurchase intentions in services, Journal of Retailing, Vol 76, 259-374
Makhoul, J., Kubala, F., Schwartz, R. ＆ Weischedel, R. (1999),Performance measures for information extraction, In proc. DARPA Broadcast News Workshop, 249-254
Makhoul, J., Kubala, F., Leek, T., Liu, D., Nguyen, L., Schwartz, R. ＆ Srivastava, A. (2000), “Speech and Language Technologies for Audio Indexing and Retrieval, Proceedings of The IEEE, Vol 88, 1338-1353
Malthus, T.R. (1798), An Essay on the Principle of Population, England： Johnson J., London
Mols, N.P. (1998), “The behavioral consequences of PC banking, International Journal of Bank Marketing, Vol 16, 195-201
Mutanen, T., Ahola, J. ＆ Nousiainen, S. (2006), “Customer Churn Prediction-A Case Study in Retail Banking, ECML/PKDD2006 Workshop, 13-18.
Niea, G., Rowec, W., Zhanga, L., Tiana, Y. ＆ Shi, Y. (2011), “Credit card churn forecasting by logistic regression and decision tree, Expert Systems with Applications, Vol 38 , 15273-15285
Paulin, M., Perrien, J., Ferguson, R.J., Salazar, A.M.A. ＆ Seruya L.M. (1998), “Relational norms and client retention: External effectiveness of commercial banking in Canada and Mexico, International Journal of Bank Marketing, Vol 16, 24-31
Pearce, J. ＆ Ferrier S. (2000), “Evaluating the predictive performance of habitat models developed using logistic regression, Vol 133, 255-245
Pfahringer, B., Holmes, G. ＆ Kirkby, R. (2001), “Optimizing the Induction of Alternating Decision Trees, Proceedings of the Fifth Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, Vol 2035, 477-487
Popovic, D. ＆ Basic, B.D. (2009), “Churn Prediction Model in Retail Banking Using Fuzzy C-Means Algorithm, Informatica, Vol 33, 243-247.
Prasad, D. ＆ Madhavi, S. (2012), “Prediction of Churn Behavior of Bank Customer Customers Using Data Mining Tools, Business Intelligence Journal, Vol 5, 96-101.
Provost, F. ＆ Fawcett, T. (1997), “Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions, AAAI Press, Vol 97, 43-48
Provost, F., Fawcett, T. ＆ Kohavi, R. (1998), “the case against accuracy estimation for comparing induction algorithms, Morgan Kaufmann, Vol 98, 445-453
Spackman, K.A. (1989), “Signal detection theory:Valuable tools for evaluating inductive learning, Workshop on Machine Learning, Vol 1, 160-163
Swets, J. A. (1988), “Measuring the accuracy of diagnostic systems, Science, Vol 240, 1285-1293.
Thomas, J.S. (2000), “A methodology for linking customer acquisition to customer retention, Journal of Marketing Research, Vol 38, 262-268
Zhao, J. ＆ Dang, X.H. (2008), “Bank Customer Churn Prediction Based on Support Vector Machine: Taking a Commercial Bank’s VIP Customer Churn as the Example, IEEE, Vol 1, 1-4