Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4970016 | Pattern Recognition Letters | 2017 | 7 Pages |
Abstract
Irreducible testors have been used to solve feature selection problems. All the exhaustive algorithms reported for the generation of irreducible testors have exponential complexity. However, several problems only require a portion of irreducible testors (only a subset of all). The hill-climbing algorithm is the latest approach that finds a subset of irreducible testors. So this paper introduces a parallel version of the hill-climbing algorithm which takes advantage of all the cores available in the graphics card because it has been developed on a CUDA platform. The proposed algorithm incorporates a novel mechanism that improves the exploration capability without adding any extra computation at the mutation step, thus increasing the rate of irreducible testors found. In addition, a Bloom filter is incorporated for efficient handling of duplicate irreducible testors. Several experiments with synthetic and real data, and a comparison with other state-of-the-art algorithms are presented in this work.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Ivan Piza-Davila, Guillermo Sanchez-Diaz, Manuel S. Lazo-Cortes, Luis Rizo-Dominguez,