Comparison of Machine-Learning Techniques for Handling Multicollinearity in Big Data Analytics and High-Performance Data Mining
Authors: Gerard Dumancas (Oklahoma Baptist University), Ghalib Bello (Virginia Commonwealth University)
Abstract: Big data analytics and high-performance data mining have become increasingly popular in various fields. They focus on the automated analysis of large-scale data, a process ideally involving minimal human input. A typical big data analytic scenario involves the use of thousands of variables, many of which will be highly correlated. Using mortality and moderately correlated lipid profile data from the NHANES database, we compared the predictive capabilities of individual parametric and nonparametric machine-learning techniques, as well as 'stacking', an ensemble learning technique. Our results indicate that partial least squares-discriminant analysis offers the best performance in the presence of multicollinearity, and that the use of stacking does not significantly improve predictive performance. The insights gained from this study could be useful in selecting machine-learning methods for automated pre-processing of thousands of correlated variables in high-performance data mining.
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