A combinational feature selection and ensemble neural network method for classification of gene expression data

Background: Microarray experiments are becoming a powerful tool for clinical diagnosis, as they have the potential to discover gene expression patterns that are characteristic for a particular disease. To date, this problem has received most attention in the context of cancer research, especially in tumor classification. Various feature selection methods and classifier design strategies also have been generally used and compared. However, most published articles on tumor classification have applied a certain technique to a certain dataset, and recently several researchers compared these techniques based on several public datasets. But, it has been verified that differently selected features reflect different aspects of the dataset and some selected features can obtain better solutions on some certain problems. At the same time, faced with a large amount of microarray data with little knowledge, it is difficult to find the intrinsic characteristics using traditional methods. In this pa...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Bing Liu, Qinghua Cui, Tianzi Jiang, Songde Ma
• Keywords: BMCBI 2004, Feature Selection, Microarray Data, Tumor Classification