Optimizing Support Vector Machine Analysis in Low Density Biological Data Sets

Abstract

We explore the effectiveness of Support Vector Machines (SVM) for classification in a sparse data set. Non-human primate models are utilized to analyze Alcohol Use Disorders (AUDs); however, the resulting data have a limited sample size. The challenge of low sample numbers and low replicates are explored using a variety of optimization strategies for feature extraction, including correlation, entropy, density, linear support vector machines for regression (SVR), backward SVR, and forward SVR. We investigate these approaches against the backdrop of the relationship between alcohol consumption and tibial bone mineral density. The results indicate that machine learning (ML) can effectively be used in cases of low and diverse biological data sets. The best relevance feature ranking strategies are correlation, SVR forward, and SVR backward.