Redundancy-Reducing Feature Selection from Microarray Data Based on Gene-Grouping

• Main Authors: Bao-wen Chang, 張寶文
• Other Authors: Jyh-Jen Horng Shiau
• Format: Others
• Language: en_US
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/ka3d5b

碩士 === 國立交通大學 === 統計學研究所 === 92 === A microarray dataset contains thousands of genes but only tens of subjects in general. This so-called “large (gene), small (subject)” feature brings about some difficulties to statistical analysis. Gene selection is a typical approach to deal with this problem. There are two conventional gene selection methods, filters and wrappers. Filters judge whether a gene should be selected based on a ranking criterion; therefore, they are very fast in computation but might select highly correlated genes that give rise to redundancy. On the other hand, wrappers usually select a small set of non-redundant genes but require extensive computation. A combination of these two methods is adopted in this study. We first filter out irrelevant genes according a ranking criterion and then group the rest to avoid redundancy via K-means clustering algorithm. Then, the SVM-RFE gene selection method proposed by Guyon et al. (2002) is applied to a list of candidate genes selected from each cluster. Three popular cancer data sets are analyzed by means of the proposed method. The results show that the proposed method performs better than three filter methods under study when the number of selected genes is small.