Speeding up multiple instance learning classification rules on GPUs

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@Article{2014-KAIS-Cano,
  author =       "Alberto Cano and Amelia Zafra and Sebastian Ventura",
  title =        "Speeding up multiple instance learning classification
                 rules on GPUs",
  journal =      "Knowledge and Information Systems",
  year =         "2015",
  volume =       "44",
  number =       "1",
  pages =        "127--145",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming,
                 Multi-instance learning, Classification, Parallel
                 computing, GPU",
  ISSN =         "0219-1377",
  DOI =          "doi:10.1007/s10115-014-0752-0",
  size =         "19 pages",
  abstract =     "Multiple instance learning is a challenging task in
                 supervised learning and data mining. However, algorithm
                 performance becomes slow when learning from large-scale
                 and high-dimensional data sets. Graphics processing
                 units (GPUs) are being used for reducing computing time
                 of algorithms. This paper presents an implementation of
                 the G3P-MI algorithm on GPUs for solving multiple
                 instance problems using classification rules. The GPU
                 model proposed is distributable to multiple GPUs,
                 seeking for its scalability across large-scale and
                 high-dimensional data sets. The proposal is compared to
                 the multi-threaded CPU algorithm with streaming SIMD
                 extensions parallelism over a series of data sets.
                 Experimental results report that the computation time
                 can be significantly reduced and its scalability
                 improved. Specifically, an speedup of up to 149 times
                 can be achieved over the multi-threaded CPU algorithm
                 when using four GPUs, and the rules interpreter
                 achieves great efficiency and runs over 108 billion
                 genetic programming operations per second.",
}

Genetic Programming entries for Alberto Cano Rojas Amelia Zafra Gomez Sebastian Ventura

Citations