An efficient SVM-GA feature selection model for large healthcare databases

Authors:
Rick Chow;Wei Zhong;Michael Blackmon;Richard Stolz;Marsha Dowell
Affiliations:
University of South Carolina Upstate, Spartanburg, SC, USA;University of South Carolina Upstate, Spartanburg, SC, USA;University of South Carolina Upstate, Spartanburg, SC, USA;University of South Carolina Upstate, Spartanburg, SC, USA;University of South Carolina Upstate, Spartanburg, SC, USA
Venue:
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Year:
2008

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Abstract

This paper presents an efficient hybrid feature selection model based on Support Vector Machine (SVM) and Genetic Algorithm (GA) for large healthcare databases. Even though SVM and GA are robust computational paradigms, the combined iterative nature of a SVM-GA hybrid system makes runtime costs infeasible when using large databases. This paper utilizes hierarchical clustering to reduce dataset size and SVM training time, multi-resolution parameter search for efficient SVM model selection, and chromosome caching to avoid redundant fitness evaluations. This approach significantly reduces runtime and improves classification performance.