(3.238.98.214) 您好!臺灣時間:2021/05/08 13:10
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:劉兆文
研究生(外文):Chao-Wen Liu
論文名稱:利用MaxF篩選變數組合之迴歸樹
論文名稱(外文):Enhanced Sample-Efficient Regression Trees with MaxF Selection Criterion and Attribute Combination Selection
指導教授:陳正剛陳正剛引用關係
指導教授(外文):Argon Chen
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:工業工程學研究所
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:66
中文關鍵詞:迴歸樹
外文關鍵詞:Regression Trees
相關次數:
  • 被引用被引用:1
  • 點閱點閱:99
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
The well-known regression trees use the variance reduction as a measure to select attributes and split the data set to build a decision tree model. The conventional tree splitting, however, depletes the sample size rapidly after few levels of splitting results in unreliable splitting decisions with small sample sizes. In order to overcome the sample-depleting problem of regression trees, Sample-efficient regression trees (SERT) was proposed to avoid the unnecessary splits. But when a great number of interaction effects exist, the select-and-split construction of SERT is still not efficient in stopping the sample size depleting. In this research, we propose an Enhanced Sample-Efficient Regression Trees (ESERT) that expended with attribute combination selection and the MaxF selection criterion.
We first show how to apply the MaxF selection criterion to regression tree’s attribute selection and stopping of tree construction. With the MaxF selection criterion, methodologies of attribute combination selection are introduced. A complete select-and-split tree construction and model estimation will be described. The ESERT procedures for both binary and continuous attributes will be developed. Using three different simulation scenarios, we demonstrate the contributions of MaxF selection criterion, sample-efficient method and attribute combination selection to tree construction. Two real cases: semiconductor bad tool selection and differentially expressed gene selection, will be also used to illustrate and validate the proposed ESERT.
Abstract i
中文摘要 ii
Contents iii
Contents of Figures iv
Contents of Tables vi
Chapter 1 Introduction 1
Chapter 2 SERT with MaxF Selection Criterion and Attribute Combination Selection for Binary Attributes 8
2.1 MaxF Selection Criterion for Regression Trees 8
2.2 Attribute Combination Selection 13
2.3 Complete Select-and-Split Tree Construction 19
2.4 Estimation 23
Chapter 3 SERT with MaxF Selection Criterion and Attribute Combination Selection for Continuous Attributes 25
3.1 MaxF Selection Criterion for Regression Trees 26
3.2 Attribute Combination Selection 30
3.3 Complete Selection-and-Split Tree Construction 34
3.4 Estimation 38
Chapter 4 Validation with Simulation and Real Case Study 40
4.1 Validation with Simulation 40
4.1.1 Scenario one 40
4.1.2 Scenario two 42
4.1.3 Scenario three 45
4.2 Validation with Real case: Semiconductor Bad tool selection 49
4.3 Validation with Real case: Differentially Expressed Gene Selection 52
Chapter 5 Conclusions 56
Reference 57
Appendix: C++ code for attribute combination selection 58
[1] Bendel R. B. and Afifi A. A., “Comparison of stopping rules in forward stepwise regression”, Journal of American Statistical Association, vol. 72, pp. 46-53, 1997.
[2] Breiman L., J. H. Friedman, R. A. Olshen and C. J. Stone, “Classification and regression trees”, Monterey, CA: Wadsworth, 1984.
[3]Kidd Lin, “Robust Test for batch-and-batch variable selection”, National Taiwan University, 2004.
[4] James Jaccard, Robert Turrisi and Choi K. Wan, “Interaction Effects in Multiple Regression”, SAGE publications, 1990.
[5] Lan H. Witten and Eibe Frank, “Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations”, 1999.
[6] Legend Fu, “Robust Test for Stepwise Selection”, National Taiwan University, 2003.
[7] Leona S. Aliken and Stephen G.. West, “Multiple Regression: Testing and Intercepting Interactions”, SAGE publications, 1991.
[8] Lulu Ho, “Sample-Efficient Regression Tree for Binary and Ordinal Attributes and Continuous Target”, National Taiwan University, 2003.
[9]Paul D. Allison, “Testing for Interaction in Multiple Regression”, The American Journal of Sociology, vol. 83, no. 1, pp. 144-153.
[10] J. Scott Armstrong and James G. Andress, “Exploratory Analysis of Marketing Data: Trees vs. Regression”, Journal of Marketing Research, pp. 487-492, 1970.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔