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指導教授(外文):Lih-Chyun Shu
中文關鍵詞:機器學習Decision TreeSVMLogistic Regression客戶流失學生轉學
外文關鍵詞:Machine learningAD-TreeSVMLogistic RegressionCustomer churnStudent transfer
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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

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