Effect of Outlier Removal on Gene Marker Selection Using Support Vector Machines

Biological markers are useful tools for the diagnosis and prognosis of disease. Many different methods are currently used to extract markers from multiple data sources, including gene expression microarrays. This paper investigates the effect of outlier removal on the performance of one such biomarker selection method, Support Vector Machines (SVM). A simple method of outlier removal is employed as a preprocessing step before the data is used for SVM analysis. Both linear and radial basis kernels are used as well as four different normalization techniques. Results show that outlier removal increases the number of highly predictive genes as well as the number of poorly predicting genes. This result thus supports the use of outlier removal prior to biological marker identification via SVM analysis.