Systems, Man, and Cybernetics

 

Home

• [P384] A GA-SVM Feature Selection Model Based on High Performance Computing Techniques
  Author(s): Tianyou Zhang, Rick Siow Mong Goh
  
  

  Abstract
  Supervised learning is well-known and widely applied in many domains including bioinformatics, cheminformatics and financial forecasting. However, the interference from irrelevant features may lead to the poor accuracy of classifiers. As a popular feature selection model, GA-SVM is desirable in many of those cases to filter out irrelevant features and improve the learning performance subsequently. However, the high computational cost strongly discourages the application of GA-SVM in large-scale datasets. In this paper, an HPC-enabled GA-SVM (HGA-SVM) is proposed by integrating data parallelization, multithreading and heuristic techniques with the ultimate goal of robustness and low computational cost. Our proposed model is comprised of four improvement strategies: 1) GA Parallelization, 2) SVM Parallelization, 3) Neighbor Search and 4) Evaluation Caching. All the four strategies improve various aspects of the feature selection model and contribute collectively towards higher computational throughput.

  
   

Full Text (PDF)