臺灣博碩士論文加值系統

近年來，基因微陣列表現資料是一個非常有效的醫學診斷工具，目前已被廣泛的用於分析基因和疾病之間的關係特性。由於基因微陣列具有高特徵維度的資料特性，使其可以同時間分析數千至數萬個基因表現特性，但也因此造成在分類計算時需要更多的運算時間。許多之前的研究指出特徵(基因)選取具有某些優點，利用特徵選取的特性針對基因微陣列選出對於分類計算具有良好成效的特徵子集合，並藉此提高分類正確率。本研究的目的即是針對基因微陣列資料選取出可以明顯提升分類正確率的基因子集合。我們提出一個結合刪除式與進化式的演算法進行兩階段的基因選取，其中結合布林運算的改良型二進位粒子族群最佳化演算法針對原始的二進位粒子族群進行改良，並當作一個新的進化演算法的特徵選取方式，利用k個最近鄰居法與支持向量機兩個分類演算法針對選出的特徵子集合進行分類計算。實驗結果顯示我們所提出的方法可以有效的選出少量的特徵子集合並獲得較高的分類結果。

The microarray is a medical diagnostic tool with good efficiency, and it was used for analyzing the behavior characteristic between the gene and disease by the extensive one at present. Microarray data are characterized by a high dimension, which could be analyzed more than thousand of genes and diseases simultaneously. However, it will lead to need more computation time when it is implemented on classification. Many previous literatures showed the feature (gene) selection has some advantage, such as gene extraction which influences classification accuracy effectively, to eliminate the useless genes and improve the calculation performance and classification accuracy. The goal of this study is to select a small set of genes which are useful to the classification task. We proposed a two-stage method using several filter methods to proceed gene ranking and combined the evolutional algorithms on gene expression data to select an optimal gene subset. In this study, an improved particle swarm optimization which introduced a Boolean function was used to improve the disadvantage of standard binary particle swarm optimization as a new evolutional algorithm for gene selection, and both k-nearest neighbor and support vector machine classifiers were used to calculate the classification accuracy. The experimental results revealed that our proposed feature selection method is able to effectively select the relevant gene subset and achieve better classification accuracy than the previous studies.

ABSTRACT IN CHINESE i
ABSTRACT ii
ACKNOWLEDGEMENT iii
TABLE OF CONTENT iv
LIST OF TABLES vi
LIST OF FIGURES vii
I. INTRODUCTION 1
1.1 Background 1
1.2 Literature Review 2
1.3 Research Purposes 4
1.4 Organization of thesis 5
II. RELATED WORK 6
2.1 Feature selection 6
2.1.1 Filter model methods 6
2.1.1.1 Information Gain 8
2.1.1.2 Gain Ratio 9
2.1.1.3 Chi-square 10
2.1.1.4 Relief-F 10
2.1.1.5 Correlation-based feature selection 11
2.1.2 Wrapper model methods 11
2.1.2.1 Sequential forward selection 12
2.1.2.2 Sequential backward elimination 13
2.1.3 Embedded methods 13
2.2 Classification algorithms 15
2.2.1 k-nearest neighbor 15
2.2.2 Support vector machine 18
2.3 Classification accuracy estimate 21
2.3.1 Holdout validation 21
2.3.2 K-fold cross-validation 22
2.3.3 Leave-one-out cross-validation 22
2.4 Evolutionary algorithm for feature selection 23
2.4.1 Evolutionary algorithm 23
2.4.2 Binary particle swarm optimization 26
III. METHODOLOGY 29
3.1 Experimental framework 29
3.2 Gene selection for microarray gene expression data 30
3.2.1 Filter model methods for gene subset selection 31
3.2.2 BPSO for gene subset selection 32
3.2.2.1 Encoding of candidate solutions 32
3.2.2.2 Fitness function 34
3.2.2.3 Termination condition 34
3.2.2.4 BPSO learning process 35
3.3 Proposed method 40
3.3.1 Boolean binary particle swarm optimization 40
3.3.2 Two-stage gene selection 44
IV. EXPERIMENT AND RESULTS 46
4.1 Experimental data 46
4.1.1 Data preprocess 47
4.2 Parameter setting 48
4.3 Result of different gene selection methods 50
4.3.1 Filter model methods for gene selection 50
4.3.2 Wrapper model methods for gene selection 58
4.3.3 Two-stage method for gene selection 73
V. DISCUSSION 100
VI. CONCLUSION 106
VII. FUTURE WORK 109
REFERENCES 111
APPENDIX A 121

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