Constructive induction and genetic algorithms for
learning concepts with complex interaction
L. Shafti, and E. P&\#233;rez. GECCO 2005: Proceedings of the 2005 conference on
Genetic and evolutionary computation, 2, page 1811--1818. Washington DC, USA, ACM Press, (25-29 June 2005)
Abstract
Constructive Induction is the process of transforming
the original representation of hard concepts with
complex interaction into a representation that
highlights regularities. Most Constructive Induction
methods apply a greedy strategy to find interacting
attributes and then construct functions over them. This
approach fails when complex interaction exists among
attributes and the search space has high variation. In
this paper, we illustrate the importance of applying
Genetic Algorithms as a global search strategy for
these methods and present MFE2/GA1, while comparing it
with other GA-based Constructive Induction methods. We
empirically analyse our Genetic Algorithm's operators
and compare MFE2/GA with greedy-based methods. We also
performed experiments to evaluate the presented method
when concept has attributes participating in more than
one complex interaction. In experiments that are
conducted, MFE2/GA successfully finds interacting
attributes and constructs functions to represent
interactions. Results show the advantage of using
Genetic Algorithms for Constructive Induction when
compared with greedy-based methods.
GECCO 2005: Proceedings of the 2005 conference on
Genetic and evolutionary computation
year
2005
month
25-29 June
pages
1811--1818
publisher
ACM Press
volume
2
organisation
ACM SIGEVO (formerly ISGEC)
publisher_address
New York, NY, 10286-1405, USA
isbn
1-59593-010-8
notes
GECCO-2005 A joint meeting of the fourteenth
international conference on genetic algorithms
(ICGA-2005) and the tenth annual genetic programming
conference (GP-2005).
ACM Order Number 910052
%0 Conference Paper
%1 1068317
%A Shafti, Leila Shila
%A P&\#233;rez, Eduardo P&\#233;rez
%B GECCO 2005: Proceedings of the 2005 conference on
Genetic and evolutionary computation
%C Washington DC, USA
%D 2005
%E Beyer, Hans-Georg
%E O'Reilly, Una-May
%E Arnold, Dirk V.
%E Banzhaf, Wolfgang
%E Blum, Christian
%E Bonabeau, Eric W.
%E Cantu-Paz, Erick
%E Dasgupta, Dipankar
%E Deb, Kalyanmoy
%E Foster, James A.
%E de
Jong, Edwin D.
%E Lipson, Hod
%E Llora, Xavier
%E Mancoridis, Spiros
%E Pelikan, Martin
%E Raidl, Guenther R.
%E Soule, Terence
%E Tyrrell, Andy M.
%E Watson, Jean-Paul
%E Zitzler, Eckart
%I ACM Press
%K Classifier Genetics-Based Learning Learning, Machine Systems, algorithms, attribute attributes construction, constructive design, experimentation, feature genetic induction, interaction, selection, shared
%P 1811--1818
%T Constructive induction and genetic algorithms for
learning concepts with complex interaction
%U http://doi.acm.org/10.1145/1068009.1068317
%V 2
%X Constructive Induction is the process of transforming
the original representation of hard concepts with
complex interaction into a representation that
highlights regularities. Most Constructive Induction
methods apply a greedy strategy to find interacting
attributes and then construct functions over them. This
approach fails when complex interaction exists among
attributes and the search space has high variation. In
this paper, we illustrate the importance of applying
Genetic Algorithms as a global search strategy for
these methods and present MFE2/GA1, while comparing it
with other GA-based Constructive Induction methods. We
empirically analyse our Genetic Algorithm's operators
and compare MFE2/GA with greedy-based methods. We also
performed experiments to evaluate the presented method
when concept has attributes participating in more than
one complex interaction. In experiments that are
conducted, MFE2/GA successfully finds interacting
attributes and constructs functions to represent
interactions. Results show the advantage of using
Genetic Algorithms for Constructive Induction when
compared with greedy-based methods.
%@ 1-59593-010-8
@inproceedings{1068317,
abstract = {Constructive Induction is the process of transforming
the original representation of hard concepts with
complex interaction into a representation that
highlights regularities. Most Constructive Induction
methods apply a greedy strategy to find interacting
attributes and then construct functions over them. This
approach fails when complex interaction exists among
attributes and the search space has high variation. In
this paper, we illustrate the importance of applying
Genetic Algorithms as a global search strategy for
these methods and present MFE2/GA1, while comparing it
with other GA-based Constructive Induction methods. We
empirically analyse our Genetic Algorithm's operators
and compare MFE2/GA with greedy-based methods. We also
performed experiments to evaluate the presented method
when concept has attributes participating in more than
one complex interaction. In experiments that are
conducted, MFE2/GA successfully finds interacting
attributes and constructs functions to represent
interactions. Results show the advantage of using
Genetic Algorithms for Constructive Induction when
compared with greedy-based methods.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Washington DC, USA},
author = {Shafti, Leila Shila and P\&\#233;rez, Eduardo P\&\#233;rez},
biburl = {https://www.bibsonomy.org/bibtex/276ca6387d7a99db81aeb9bd1fb28ab3f/brazovayeye},
booktitle = {{GECCO 2005}: Proceedings of the 2005 conference on
Genetic and evolutionary computation},
editor = {Beyer, Hans-Georg and O'Reilly, Una-May and Arnold, Dirk V. and Banzhaf, Wolfgang and Blum, Christian and Bonabeau, Eric W. and Cantu-Paz, Erick and Dasgupta, Dipankar and Deb, Kalyanmoy and Foster, James A. and {de
Jong}, Edwin D. and Lipson, Hod and Llora, Xavier and Mancoridis, Spiros and Pelikan, Martin and Raidl, Guenther R. and Soule, Terence and Tyrrell, Andy M. and Watson, Jean-Paul and Zitzler, Eckart},
interhash = {09ee73ce8f7a6ca8c5a71c549f7832d5},
intrahash = {76ca6387d7a99db81aeb9bd1fb28ab3f},
isbn = {1-59593-010-8},
keywords = {Classifier Genetics-Based Learning Learning, Machine Systems, algorithms, attribute attributes construction, constructive design, experimentation, feature genetic induction, interaction, selection, shared},
month = {25-29 June},
notes = {GECCO-2005 A joint meeting of the fourteenth
international conference on genetic algorithms
(ICGA-2005) and the tenth annual genetic programming
conference (GP-2005).
ACM Order Number 910052},
organisation = {ACM SIGEVO (formerly ISGEC)},
pages = {1811--1818},
publisher = {ACM Press},
publisher_address = {New York, NY, 10286-1405, USA},
timestamp = {2008-06-19T17:51:31.000+0200},
title = {Constructive induction and genetic algorithms for
learning concepts with complex interaction},
url = {http://doi.acm.org/10.1145/1068009.1068317},
volume = 2,
year = 2005
}