Metalearning for Gene Expression Data Classification

Machine Learning techniques have been largely applied to the problem of class prediction in microarray data. Nevertheless, current approaches to select appropriate methods for such task often result unsatisfactory in many ways, instigating the need for the development of tools to automate the process. In this context, the authors introduce the use of metalearning in the specific domain of gene expression classification. Experiments with the KNN-ranking method for algorithm recommendation applied for 49 datasets yielded successful results.