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研究生:楊明淳
研究生(外文):Ming-Chun Yang
論文名稱:建立在位元映射索引上的特徵選取方法
論文名稱(外文):The Efficient Feature Selection Methods based on Bitmap Indexing Approach
指導教授:曾憲雄曾憲雄引用關係
指導教授(外文):Shian-Shyong Tseng
學位類別:碩士
校院名稱:國立交通大學
系所名稱:資訊科學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:68
中文關鍵詞:案例式推論特徵選取位元映射索引約略集合
外文關鍵詞:CBRfeature selectionbitmap indexingrough set
相關次數:
  • 被引用被引用:1
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  • 下載下載:20
  • 收藏至我的研究室書目清單書目收藏:0
案例式推論是利用過去的案例和經驗來解決目前問題的技術。但是由於知識不容易被完整地呈現,使得如何找到足夠的特徵來充分地呈現案例特性成為一個關鍵的問題。在這篇論文中我們針對最佳解和近似最佳解,分別提出了以位元映射為基礎之特徵選取法及輔以辨識矩陣之位元映射特徵選取法。利用位元映射索引技術和約略集合的觀念,在現有的資料庫中自動選取重要的欄位。同時針對這二種方法,我們也提供了明確的定義及相對應的演算法。最後,經由和相關特徵選取技術的實驗比較結果,證明我們提出的特徵選取方法能以更快速的方式提供了正確的特徵選取結果。
CBR(Case-Based Reasoning) is a problem solving technique that reuses past cases and experiences to find a solution to current problems. A critical issue in case-based reasoning is to select the correct and enough features to represent a case. However, this task is difficult to carry out since such knowledge is often exhaustively captured and cannot be represented successfully. In this thesis, the new, efficient feature selection methods originated from bitmap indexing and rough set techniques will be proposed. There are two methods, including bitmap-based feature selection method and bitmap-based feature selection method with discernibility matrix, are proposed for discovering the optimal and nearly optimal feature sets for decision—making problems. And the corresponding indexing and selecting algorithms for such feature selection methods are also proposed. Finally, some experiments and comparisons are given and the result shows the efficiency and accuracy of our proposed methods.
ABSTRACT(IN CHINESE) I
ABSTRACT II
ACKNOWLEDGEMENT III
CONTENTS IV
LIST OF FIGURES V
LIST OF ALGORITHMS VI
CHAPTER 1. INTRODUCTION 1
1.1 MOTIVATION 1
1.2 CONTRIBUTIONS 2
1.3 READER’S GUIDE 3
CHAPTER 2. RELATED WORK 4
2.1 CASE-BASED REASONING 4
2.2 FEATURE SELECTION 5
2.3 ROUGH SET THEORY 6
2.4 BITMAP INDEXING TECHNOLOGY 7
CHAPTER 3. BITMAP-BASED FEATURE SELECTION METHOD 9
3.1 PROBLEM DEFINITION 9
3.2 BITMAP INDEXING PHASE 12
3.3 FEATURE SELECTION PHASE 20
3.3.1 Cleansing 20
3.3.2 Feature selection 31
3.3.3 Feature combination 33
CHAPTER 4. BITMAP-BASED FEATURE SELECTION METHOD WITH DISCERNIBILITY MATRIX 39
4.1 PROBLEM DEFINITION 39
4.2 FEATURE SELECTION PHASE 41
CHAPTER 5. EXPERIMENT 52
5.1 DATASET DESCRIPTION 52
5.2 COMPARISONS 54
5.2.1 Optimal solution 54
5.2.2 Nearly optimal solution 56
CHAPTER 6. CONCLUSION AND FUTURE WORK 60
REFERENCE 61
[1] UCI Repository: http://www.ics.uci.edu/~mlearn/MLRepository.html.
[2] R. Barletta, "An introduction to case-based reasoning," AI Expert, Vol. 6, No.8, pp.42-49, 1991.
[3] W. C. Chen, S. S. Tseng, J. H. Chen, M. F. Jiang, "A framework of feature selection for the case-based reasoning," Systems, Man, and Cybernetics, 2000 IEEE International Conference on , Vol. 1 , pp.1-5, 2000.
[4] A. Gonzalez, R. Perez, "Selection of relevant features in a fuzzy genetic learning," IEEE Transaction on SMC-Part B, Vol. 31, No. 3, June 2001.
[5] M. Last, A. Kandel, O. Maimon, "Information theoretic algorithm for feature selection," Pattern Recognition Letter, Vol. 22, pp.799-811, 2001.
[6] H. M. Lee, C. M. Chen, J. M. Chen, Y. L. Jou, "An efficient fuzzy classifier with feature selection based on fuzzy entropy," IEEE Transaction on SMC-Part B, Vol. 27, No. 2, April 1997.
[7] H. Liu, R. Setiono, "Incremental feature selection," Applied Intelligence 9, pp.217-230, 1998.
[8] Z. Pawlak, "Rough set," International Journal of Computer and Information Sciences, pp.341-356, 1982.
[9] Z. Pawlak, Rough Sets, Theoretical Aspects of Reasoning about Data, Boston, MA: Kluwer Academic Publishers, 1991.
[10] J. Quinlan, "Introduction of decision trees," Machine Learning, Vol. 1, No. 1, pp.81-106, 1986.
[11] M. L. Raymer, W. F. Punch, E. D. Goodman, L. A. Kuhn, "Dimensionality reduction using genetic algorithm," IEEE Transaction on Evolutionary Computation, Vol. 4, No. 2, July 2000. [12] A. Skowron, C. Rauszer, "The discernibility matrics and functions in information systems," Intelligent Decision Support, pp.331-362, 1992.
[13] T. Munakata, Fundamentals of the new artificial intelligence: beyond traditional paradigms, New York /Springer, pp.140-182, 1998.
[14] I. Watson, "Case-based reasoning is a methodology not a technology," Knowledge-Based Systems, Vol. 12, pp.303-308, 1999.
[15] T. T. Wang, Knowledge acquisition from quantitative data based on the rough set theory, master thesis, Department of Information Engineering I-Shou University, 2000.
[16] M. C. Wu, P. B. Alejandro, "Encoded bitmap indexing for data warehouses master thesis," Data Engineering 1998 Proceedings, pp.220-230, 1998.
[17] Y. Yang, T. C. Chiam, "Rule discovery based on rough set theory," Information Fusion, FUSION 2000. Proceedings of the Third International Conference on, Vol. 1, pp.11-16, 2000.
[18] N. Zhong, J. Dong, S. Ohsuga, "Using rough sets with heuristics for feature selection," Journal of Intelligent Systems, Vol. 16, pp.199-214, 2001.
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