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研究生:Muhammad Nashrullah
研究生(外文):Muhammad Nashrullah
論文名稱:Building Knowledge-based Decision Tree by SVM and Entropy
論文名稱(外文):藉由SVM和Entropy建構知識型之決策樹
指導教授:Yo-Ping Huang
口試委員:楊棧雲蘇國和洪茂盛黃正民
口試日期:2016-07-15
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:電機工程系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
畢業學年度:104
中文關鍵詞:machine learningdecision treeInformation gain
外文關鍵詞:SVM
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In recent years machine learning has become a popular research topic. There are many applications implemented by the combinations of different techniques such as Support Vector Machine (SVM), k-Nearest Neighbors (KNN), and deep learning in obtrusive platforms that include the domains of healthcare, economy and agriculture. Healthcare researchers have built application systems to help clinicians diagnose diseases. However, some models lack flexibility to interpret the knowledge as if clinician’s indulgence. To overcome such problems, we use support vector machines, one of the supervised learning algorithms with kernel radial basis function (RBF) as a nonlinear classification model, to separate the hyperplane and maximize the margin that can tolerate small errors. From those correctly classified patterns from SVM model, we proceed to find a better split point for the next step. Split point is used to calculate information gain that can be applied to select principal features among all attributes. Finally, we construct the knowledge-based decision tree from the ordered information gain to classify the unknown medical patterns. Simulation results from different data sets verify that the proposed model is effective and feasible for the classification of medical databases.
Contents
ABSTRACT i
ACKNOWLEDGMENTS iii
List of Figures vi
List of Tables vii
Chapter 1 Introduction 1
1.1 Background 1
1.2 Previous Related Work 1
1.3 Problem Statement 3
1.4 Thesis Development 3
Chapter 2 Literature Review 5
2.1 Introduction 5
2.2 Support Vector Machine 5
2.3 Cross validation 7
2.4 Entropy 8
2.5 Decision Tree 9
2.6 Performance measurements 9
2.7 Missing Values 11
2.7.1 Types of Missing Values 11
2.7.2 Handling Missing Data 11
Chapter 3 Methods 13
3.1 Introduction 13
3.2 Data Set 14
3.2.1 Mammographic Mass Data Set 14
3.2.2 Vertebral Column Data Set 14
3.2.3 Diabetic Retinopathy Debrecen Data Set 15
3.3 Support Vector Machines 15
3.4 Observation of split points. 19
3.4.1 Observation in Mammographic Mass Data Set 21
3.4.2 Observation in Vertebral Column Data Set 23
3.4.3 Observation in Diabetic Retinopathy Debrecen Data Set 26
3.5 Calculation of Information Gain. 28
Chapter 4 Results and Discussion 33
4.1 Result of Mammographic Mass Data Set 33
4.2 Result of Vertebral Column Data Set 35
4.3 Result of Diabetic Retinopathy Debrecen Data Set 37
Chapter 5 Conclusions and Future Works 40
References 41
About the Author 44
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