Speeding up multiple instance learning classification rules on GPUs
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @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