Gene Feature Extraction Using T-Test Statistics and Kernel Partial Least Squares

Abstract

AbstractIn this paper, we propose a gene extraction method by using two standard feature extraction methods, namely the T-test method and kernel partial least squares (KPLS), in tandem. First, a preprocessing step based on the T-test method is used to filter irrelevant and noisy genes. KPLS is then used to extract features with high information content. Finally, the extracted features are fed into a classifier. Experiments are performed on three benchmark datasets: breast cancer, ALL/AML leukemia and colon cancer. While using either the T-test method or KPLS does not yield satisfactory results, experimental results demonstrate that using these two together can significantly boost classification accuracy, and this simple combination can obtain state-of-the-art performance on all three datasets.KeywordsSupport Vector MachineKernel MatrixGene Selection MethodGene ExtractionLeukemia DatasetThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.