As the computing world moves from the information age
into the knowledge-based age, it is beneficial to
induce knowledge from the information super highway
formed from the Internet and intranet. The knowledge
acquired can be expressed in different knowledge
representations such as computer programs, first-order
logical relations, or Fuzzy Petri Nets (FPNs). In this
paper, we present a flexible knowledge discovery system
called GGP (Generic Genetic Programming) that applies
genetic programming and logic grammars to learn
knowledge in various knowledge representation
formalisms. An experiment is performed to demonstrate
that GGP can discover knowledge represented in FPNs
that support fuzzy and approximate reasoning. To
evaluate the performance of GGP in producing good FPNs,
the classification accuracy of the fuzzy Petri net
induced by GGP and that of the decision tree generated
by C4.5 are compared. Moreover, the performance of GGP
in inducing logic programs from noisy examples is
evaluated. A detailed comparison to FOIL, a system that
induces logic programs, has been conducted. These
experiments demonstrate that GGP is a promising
alternative to other knowledge discovery systems and
sometimes is superior for handling noisy and inexact
data.
%0 Journal Article
%1 Wong:2001:DSS
%A Wong, Man Leung
%D 2001
%J Decision Support Systems
%K Databases, Discovery Fuzzy Grammars, Knowledge Logic Nets Petri algorithms, genetic in programming,
%P 405--428
%R doi:10.1016/S0167-9236(01)00092-6
%T A Flexible Knowledge Discovery System using Genetic
Programming and Logic Grammars
%U http://www.sciencedirect.com/science/article/B6V8S-43W051G-2/2/e504e5d59385b792e3c424bd5bb4d003
%V 31
%X As the computing world moves from the information age
into the knowledge-based age, it is beneficial to
induce knowledge from the information super highway
formed from the Internet and intranet. The knowledge
acquired can be expressed in different knowledge
representations such as computer programs, first-order
logical relations, or Fuzzy Petri Nets (FPNs). In this
paper, we present a flexible knowledge discovery system
called GGP (Generic Genetic Programming) that applies
genetic programming and logic grammars to learn
knowledge in various knowledge representation
formalisms. An experiment is performed to demonstrate
that GGP can discover knowledge represented in FPNs
that support fuzzy and approximate reasoning. To
evaluate the performance of GGP in producing good FPNs,
the classification accuracy of the fuzzy Petri net
induced by GGP and that of the decision tree generated
by C4.5 are compared. Moreover, the performance of GGP
in inducing logic programs from noisy examples is
evaluated. A detailed comparison to FOIL, a system that
induces logic programs, has been conducted. These
experiments demonstrate that GGP is a promising
alternative to other knowledge discovery systems and
sometimes is superior for handling noisy and inexact
data.
@article{Wong:2001:DSS,
abstract = {As the computing world moves from the information age
into the knowledge-based age, it is beneficial to
induce knowledge from the information super highway
formed from the Internet and intranet. The knowledge
acquired can be expressed in different knowledge
representations such as computer programs, first-order
logical relations, or Fuzzy Petri Nets (FPNs). In this
paper, we present a flexible knowledge discovery system
called GGP (Generic Genetic Programming) that applies
genetic programming and logic grammars to learn
knowledge in various knowledge representation
formalisms. An experiment is performed to demonstrate
that GGP can discover knowledge represented in FPNs
that support fuzzy and approximate reasoning. To
evaluate the performance of GGP in producing good FPNs,
the classification accuracy of the fuzzy Petri net
induced by GGP and that of the decision tree generated
by C4.5 are compared. Moreover, the performance of GGP
in inducing logic programs from noisy examples is
evaluated. A detailed comparison to FOIL, a system that
induces logic programs, has been conducted. These
experiments demonstrate that GGP is a promising
alternative to other knowledge discovery systems and
sometimes is superior for handling noisy and inexact
data.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Wong, Man Leung},
biburl = {https://www.bibsonomy.org/bibtex/2ab5867fdb1a71d1e3299255b61367f03/brazovayeye},
doi = {doi:10.1016/S0167-9236(01)00092-6},
interhash = {d65b28849df37e83e22fdc0963a88425},
intrahash = {ab5867fdb1a71d1e3299255b61367f03},
journal = {Decision Support Systems},
keywords = {Databases, Discovery Fuzzy Grammars, Knowledge Logic Nets Petri algorithms, genetic in programming,},
pages = {405--428},
timestamp = {2008-06-19T17:54:24.000+0200},
title = {A Flexible Knowledge Discovery System using Genetic
Programming and Logic Grammars},
url = {http://www.sciencedirect.com/science/article/B6V8S-43W051G-2/2/e504e5d59385b792e3c424bd5bb4d003},
volume = 31,
year = 2001
}