This paper proposes a novel method called FLGP to
construct a classifier device of capability in feature
selection and feature extraction. FLGP is developed
with layered genetic programming that is a kind of the
multiple-population genetic programming. Populations
advance to an optimal discriminant function to divide
data into two classes. Two methods of feature selection
are proposed. New features extracted by certain layer
are used to be the training set of next layer's
populations. Experiments on several well-known datasets
are made to demonstrate performance of FLGP.
a Department of Computer Science, National Chiao Tung
University, 1001 Ta Hsueh Road, HsinChu 300, Taiwan
b Library and Institute of Information Management,
National Chiao Tung University, Taiwan
c Department of Computer Science and Information
Engineering, National University of Tainan, Taiwan
d Department of Information Management, National Dong
Hwa University, Taiwan
%0 Journal Article
%1 Lin:2007:ESA
%A Lin, Jung-Yi
%A Ke, Hao-Ren
%A Chien, Been-Chian
%A Yang, Wei-Pang
%D 2007
%J Expert Systems with Applications
%K Feature Layered Multi-population Pattern algorithms, classification, generation, genetic programming programming, selection,
%R doi:10.1016/j.eswa.2007.01.006
%T Classifier design with feature selection and feature
extraction using layered genetic programming
%X This paper proposes a novel method called FLGP to
construct a classifier device of capability in feature
selection and feature extraction. FLGP is developed
with layered genetic programming that is a kind of the
multiple-population genetic programming. Populations
advance to an optimal discriminant function to divide
data into two classes. Two methods of feature selection
are proposed. New features extracted by certain layer
are used to be the training set of next layer's
populations. Experiments on several well-known datasets
are made to demonstrate performance of FLGP.
@article{Lin:2007:ESA,
abstract = {This paper proposes a novel method called FLGP to
construct a classifier device of capability in feature
selection and feature extraction. FLGP is developed
with layered genetic programming that is a kind of the
multiple-population genetic programming. Populations
advance to an optimal discriminant function to divide
data into two classes. Two methods of feature selection
are proposed. New features extracted by certain layer
are used to be the training set of next layer's
populations. Experiments on several well-known datasets
are made to demonstrate performance of FLGP.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Lin, Jung-Yi and Ke, Hao-Ren and Chien, Been-Chian and Yang, Wei-Pang},
biburl = {https://www.bibsonomy.org/bibtex/27eed05a1347bfb518470d43efeb729be/brazovayeye},
doi = {doi:10.1016/j.eswa.2007.01.006},
interhash = {8afb821d794ddad380ee7732e95773e3},
intrahash = {7eed05a1347bfb518470d43efeb729be},
journal = {Expert Systems with Applications},
keywords = {Feature Layered Multi-population Pattern algorithms, classification, generation, genetic programming programming, selection,},
note = {Article in Press},
notes = {a Department of Computer Science, National Chiao Tung
University, 1001 Ta Hsueh Road, HsinChu 300, Taiwan
b Library and Institute of Information Management,
National Chiao Tung University, Taiwan
c Department of Computer Science and Information
Engineering, National University of Tainan, Taiwan
d Department of Information Management, National Dong
Hwa University, Taiwan},
timestamp = {2008-06-19T17:45:37.000+0200},
title = {Classifier design with feature selection and feature
extraction using layered genetic programming},
year = 2007
}