Improving the prediction of the clinical outcome of breast cancer using evolutionary algorithms

作     者：Wahde, M Szallasi, Z 

作者机构：Chalmers Univ Technol Dept Machine & Vehicle Syst S-41296 Gothenburg Sweden Harvard Univ Sch Med Childrens Hosp Informat Program Boston MA 02115 USA 

出 版 物：《SOFT COMPUTING》 (Soft Comput.)

年 卷 期：2006年第10卷第4期

页      面：338-345页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：data classification breast cancer evolutionary algorithms 

摘      要：There exist several methods for binary classification of gene expression data sets. However, in the majority of published methods, little effort has been made to minimize classifier complexity. In view of the small number of samples available in most gene expression data sets, there is a strong motivation for minimizing the number of free parameters that must be fitted to the data. In this paper, a method is introduced for evolving (using an evolutionary algorithm) simple classifiers involving a minimal subset of the available genes. The classifiers obtained by this method perform well, reaching 97% correct classification of clinical outcome on training samples from the breast cancer data set published by van t Veer, and up to 89% correct classification on validation samples from the same data set, easily outperforming previously published results.