Multi-objective genetic programming for feature extraction and data visualization
Created by W.Langdon from
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- @Article{Cano:2016:SC,
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author = "Alberto Cano and Sebastian Ventura and
Krzysztof J. Cios",
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title = "Multi-objective genetic programming for feature
extraction and data visualization",
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journal = "Soft Computing",
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year = "2017",
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volume = "21",
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number = "8",
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pages = "2069--2089",
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month = apr,
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keywords = "genetic algorithms, genetic programming,
Classification, Feature extraction, Visualization",
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ISSN = "1433-7479",
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DOI = "doi:10.1007/s00500-015-1907-y",
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size = "21 pages",
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abstract = "Feature extraction transforms high-dimensional data
into a new subspace of lower dimensionality while
keeping the classification accuracy. Traditional
algorithms do not consider the multi-objective nature
of this task. Data transformations should improve the
classification performance on the new subspace, as well
as to facilitate data visualization, which has
attracted increasing attention in recent years.
Moreover, new challenges arising in data mining, such
as the need to deal with imbalanced data sets call for
new algorithms capable of handling this type of data.
This paper presents a Pareto-based multi-objective
genetic programming algorithm for feature extraction
and data visualization. The algorithm is designed to
obtain data transformations that optimize the
classification and visualization performance both on
balanced and imbalanced data. Six classification and
visualization measures are identified as objectives to
be optimized by the multi-objective algorithm. The
algorithm is evaluated and compared to 11 well-known
feature extraction methods, and to the performance on
the original high-dimensional data. Experimental
results on 22 balanced and 20 imbalanced data sets show
that it performs very well on both types of data, which
is its significant advantage over existing feature
extraction algorithms.",
- }
Genetic Programming entries for
Alberto Cano Rojas
Sebastian Ventura
Krzysztof J Cios
Citations