Optimal column subset selection for image classification by genetic algorithms
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @Article{Kromer2018205,
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author = "Pavel Kromer and Jan Platos and Jana Nowakova and
Vaclav Snasel",
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title = "Optimal column subset selection for image
classification by genetic algorithms",
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journal = "Annals of Operations Research",
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year = "2018",
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volume = "265",
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number = "2",
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pages = "205--222",
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keywords = "genetic algorithms, genetic programming,
Classification, Column subset selection, Dimensionality
reduction, Feature selection, Galgorithms",
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URL = "https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991018113&doi=10.1007%2fs10479-016-2331-0&partnerID=40&md5=0d6022f913b66428010164c45edd4bea",
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DOI = "doi:10.1007/s10479-016-2331-0",
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source = "Scopus",
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affiliation = "Department of Computer Science, Faculty of Electrical
Engineering and Computer Science, VSB-Technical
University of Ostrava, 17. Listopadu 15/2172,
Ostrava-Poruba, Czech Republic",
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abstract = "Many problems in operations research can be solved by
combinatorial optimization. Fixed-length subset
selection is a family of combinatorial optimization
problems that involve selection of a set of unique
objects from a larger superset. Feature selection,
p-median problem, and column subset selection problem
are three examples of hard problems that involve search
for fixed-length subsets. Due to their high complexity,
exact algorithms are often infeasible to solve
real-world instances of these problems and approximate
methods based on various heuristic and metaheuristic
(e.g. nature-inspired) approaches are often employed.
Selecting column subsets from massive data matrices is
an important technique useful for construction of
compressed representations and low rank approximations
of high-dimensional data. Search for an optimal subset
of exactly k columns of a matrix, Am by n, k less than
n, is a well-known hard optimization problem with
practical implications for data processing and mining.
It can be used for unsupervised feature selection,
dimensionality reduction, data visualization, and so
on. A compressed representation of raw real-world data
can contribute, for example, to reduction of algorithm
training times in supervised learning, to elimination
of overfitting in classification and regression, to
facilitation of better data understanding, and to many
other benefits. This paper proposes a novel genetic
algorithm for the column subset selection problem and
evaluates it in a series of computational experiments
with image classification. The evaluation shows that
the proposed modifications improve the results obtained
by artificial evolution.",
- }
Genetic Programming entries for
Pavel Kromer
Jan Platos
Jana Nowakova
Vaclav Snasel
Citations