A Filter Approach to Multiple Feature Construction for Symbolic Learning Classifiers Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @Article{Neshatian:2012:ieeeTEC,
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author = "Kourosh Neshatian and Mengjie Zhang and
Peter Andreae",
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title = "A Filter Approach to Multiple Feature Construction for
Symbolic Learning Classifiers Using Genetic
Programming",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2012",
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volume = "16",
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number = "5",
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pages = "645--661",
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month = oct,
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keywords = "genetic algorithms, genetic programming,
classification, decision trees, feature construction,
rule-based systems",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/TEVC.2011.2166158",
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size = "17 pages",
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abstract = "Feature construction is an effort to transform the
input space of classification problems in order to
improve the classification performance. Feature
construction is particularly important for classifier
inducers that cannot transform their input space
intrinsically. This article proposes GPMFC, a multiple
feature construction system for classification problems
using genetic programming (GP). The article takes a
non-wrapper approach by introducing a filter-based
measure of goodness for constructed features. The
constructed, high-level features are functions of
original input features. These functions are evolved by
GP using an entropy-based fitness function that
maximises the purity of class intervals. A decomposable
objective function is proposed so that the system is
able to construct multiple high-level features for each
problem. The constructed features are used to transform
the original input space to a new space with better
separability. Extensive experiments are conducted on a
number of benchmark problems and symbolic learning
classifiers. The results show that, in most cases, the
new approach is highly effective in increasing the
classification performance in rule-based and decision
tree classifiers. The constructed features help improve
the learning performance of symbolic learners. The
constructed features, however, may lack
intelligibility.",
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notes = "Also known as \cite{6151112}",
- }
Genetic Programming entries for
Kourosh Neshatian
Mengjie Zhang
Peter Andreae
Citations