SCHEDULE: NOV 15-20, 2015

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Comparison of Machine-Learning Techniques for Handling Multicollinearity in Big Data Analytics and High-Performance Data Mining

SESSION: Regular & ACM Student Research Competition Poster Reception

EVENT TYPE: Posters, Receptions, ACM Student Research Competition

EVENT TAG(S): HPC Beginner Friendly, Regular Poster

TIME: 5:15PM - 7:00PM

SESSION CHAIR(S): Michela Becchi, Manish Parashar, Dorian C. Arnold

AUTHOR(S):Gerard Dumancas, Ghalib Bello

ROOM:Level 4 - Lobby

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.

Chair/Author Details:

Michela Becchi, Manish Parashar, Dorian C. Arnold (Chair) - University of Missouri|Rutgers University|University of New Mexico|