An Effective Microarray Data Classifier Based on Gene Expression Programming
Lei Duan, Changjie Tang, Liang Tang, Jie Zuo, Tianqing Zhang · 2009
Applying data mining algorithms to microarray data analysis is an interesting and promising work. Gene Expression Programming (GEP) is a new development of evolution computation. GEP performs global search and discover the classification discriminant with high accuracy. However, it is undesirable to apply GEP on microarray classification directly, since the evolution efficiency of GEP is low when the number of attributes of training data is huge. To solve this problem, the main contributions of this paper include: (1) analyzing the difficulties of applying GEP to classifying microarray data directly, (2) designing a novel method to select GEP terminals from genes of microarray data, (3) proposing a method of constructing GEP classifier committee to improve the classification accuracy, (4) demonstrating the effectiveness of proposed algorithms by extensive experiments on several microarray data. Compared with some typical classification methods, the accuracy is increased as high as 10.46% in average.