A parallel classification and feature reduction method for biomedical applications
Pages 1210 - 1219
Abstract
Classification is one of the most widely used methods in data mining, with numerous applications in biomedicine. The scope and the resolution of data involved in many real life applications require very efficient implementations of classification methods, developed to run on parallel or distributed computational systems. In this study we describe SVD-ReGEC, a fully parallel implementation, for distributed memory multicomputers, of a classification algorithm with a feature reduction. The classification is based on Regularized Generalized Eigenvalue Classifier (ReGEC) and the preprocessing stage is a filter method algorithm based on Singular Value Decomposition (SVD), that reduces the dimension of the space in which classification is accomplished. The implementation is tested on random datasets and results are discussed using standard parameters.
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- A parallel classification and feature reduction method for biomedical applications
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Published In
September 2007
1413 pages
ISBN:3540681051
Sponsors
- Microsoft Corp.
- Intel: Intel
- Action S.A.
- SIAM: Society for Industrial and Applied Mathematics
- IBM Corporation
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 09 September 2007
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