Horng, Jorng TzongJorng TzongHorngWu, Li ChengLi ChengWuLiu, Baw JuineBaw JuineLiuKuo, Jun LiJun LiKuoWEN-HUNG KUOZhang, Jin JianJin JianZhang2023-11-222023-11-222009-07-0109574174https://scholars.lib.ntu.edu.tw/handle/123456789/637299Gene selection can help the analysis of microarray gene expression data. However, it is very difficult to obtain a satisfactory classification result by machine learning techniques because of both the curse-of-dimensionality problem and the over-fitting problem. That is, the dimensions of the features are too large but the samples are too few. In this study, we designed an approach that attempts to avoid these two problems and then used it to select a small set of significant biomarker genes for diagnosis. Finally, we attempted to use these markers for the classification of cancer. This approach was tested the approach on a number of microarray datasets in order to demonstrate that it performs well and is both useful and reliable. © 2008 Elsevier Ltd. All rights reserved.enBioinformaticsDecision treeExpert systemMachine learningMicroarray gene expressionjournal article10.1016/j.eswa.2008.12.0372-s2.0-60849106319https://api.elsevier.com/content/abstract/scopus_id/60849106319