we use genetic programming for changing the
representation of the input data for machine learners.
In particular, the topic of interest here is feature
construction in the learning-from-examples paradigm,
where new features are built based on the original set
of attributes. The paper first introduces the general
framework for GP-based feature construction. Then, an
extended approach is proposed where the useful
components of representation (features) are preserved
during an evolutionary run, as opposed to the standard
approach where valuable features are often lost during
search. Finally, we present and discuss the results of
an extensive computational experiment carried out on
several reference data sets. The outcomes show that
classifiers induced using the representation enriched
by the GP-constructed features provide better accuracy
of classification on the test set. In particular, the
extended approach proposed in the paper proved to be
able to outperform the standard approach on some
benchmark problems on a statistically significant
level.
%0 Journal Article
%1 krawiec:2002:GPEM
%A Krawiec, Krzysztof
%D 2002
%J Genetic Programming and Evolvable Machines
%K algorithms, change construction, feature genetic learning, machine of programming, representation, selection
%N 4
%P 329--343
%R doi:10.1023/A:1020984725014
%T Genetic Programming-based Construction of Features for
Machine Learning and Knowledge Discovery Tasks
%V 3
%X we use genetic programming for changing the
representation of the input data for machine learners.
In particular, the topic of interest here is feature
construction in the learning-from-examples paradigm,
where new features are built based on the original set
of attributes. The paper first introduces the general
framework for GP-based feature construction. Then, an
extended approach is proposed where the useful
components of representation (features) are preserved
during an evolutionary run, as opposed to the standard
approach where valuable features are often lost during
search. Finally, we present and discuss the results of
an extensive computational experiment carried out on
several reference data sets. The outcomes show that
classifiers induced using the representation enriched
by the GP-constructed features provide better accuracy
of classification on the test set. In particular, the
extended approach proposed in the paper proved to be
able to outperform the standard approach on some
benchmark problems on a statistically significant
level.
@article{krawiec:2002:GPEM,
abstract = {we use genetic programming for changing the
representation of the input data for machine learners.
In particular, the topic of interest here is feature
construction in the learning-from-examples paradigm,
where new features are built based on the original set
of attributes. The paper first introduces the general
framework for GP-based feature construction. Then, an
extended approach is proposed where the useful
components of representation (features) are preserved
during an evolutionary run, as opposed to the standard
approach where valuable features are often lost during
search. Finally, we present and discuss the results of
an extensive computational experiment carried out on
several reference data sets. The outcomes show that
classifiers induced using the representation enriched
by the GP-constructed features provide better accuracy
of classification on the test set. In particular, the
extended approach proposed in the paper proved to be
able to outperform the standard approach on some
benchmark problems on a statistically significant
level.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Krawiec, Krzysztof},
biburl = {https://www.bibsonomy.org/bibtex/2a62a0de233fb20b07509aa07db934c33/brazovayeye},
doi = {doi:10.1023/A:1020984725014},
interhash = {c36bdab83bcd6b52684174f007579a4e},
intrahash = {a62a0de233fb20b07509aa07db934c33},
issn = {1389-2576},
journal = {Genetic Programming and Evolvable Machines},
keywords = {algorithms, change construction, feature genetic learning, machine of programming, representation, selection},
month = {December},
notes = {Article ID: 5103872},
number = 4,
pages = {329--343},
timestamp = {2008-06-19T17:44:23.000+0200},
title = {Genetic Programming-based Construction of Features for
Machine Learning and Knowledge Discovery Tasks},
volume = 3,
year = 2002
}