Sparse Learner Boosting for Gene Expression Data

Gene expression analysis is commonly used to analyze millions of gene expression data points. Challenging in this process has been the development of appropriate statistical methods for high-dimensional data. We propose Sparse Learner Boosting for gene expression data analysis. Boosting is performed to minimize the loss function, although this process can cause overfitting when a large number of variables are present. Ordinary boosting utilizes all of the potential weak learners in a given data set and constructs a decision rule. The fundamental idea of Sparse Learner Boosting is to reduce the complexity of the decision rule by using fewer weak learners than is usually required. This reduction prevents overfitting and improves performance during classification. Numerical studies support this modification for high-dimensional data, such as that obtained from gene expression analysis. We show that the proposed modification improves the performance of ordinary boosting methods.