Classification of DNA Microarray Data with Random Forests

Abstract

The article includes information about the advantages of Random Forests in DNA microarray data classification. The experiment presented as a background for the publication was performed on the data devoted to Barrett’s Esophagus and two types of Reflux Disease - Erosive and Nonerosive. An original idea of estimation of a quality of the classification evolved during studies on the problem and resulted in many interesting conclusions. There are presented topics such as advantages of Random Forests in supervised DNA microarray analysis, application of bootstrap resampling used for calculation of average quality results and comparison of classification quality for Random Forests, Support Vector Machines and Linear Discriminant Analysis. Proposed solutions are said to be a good measure of quality of classification with Random Forests method.