Evolving accurate and compact classification rules with gene expression programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{ChiZhou:2003:TEC,
-
author = "Chi Zhou and Weimin Xiao and Thomas M. Tirpak and
Peter C. Nelson",
-
title = "Evolving accurate and compact classification rules
with gene expression programming",
-
journal = "IEEE Transactions on Evolutionary Computation",
-
year = "2003",
-
volume = "7",
-
number = "6",
-
pages = "519--531",
-
month = dec,
-
keywords = "genetic algorithms, genetic programming,
classification rule, data mining, gene expression
programming, GEP",
-
ISSN = "1089-778X",
-
DOI = "doi:10.1109/TEVC.2003.819261",
-
size = "13 pages",
-
abstract = "Classification is one of the fundamental tasks of data
mining. Most rule induction and decision tree
algorithms perform local, greedy search to generate
classification rules that are often more complex than
necessary. Evolutionary algorithms for pattern
classification have recently received increased
attention because they can perform global searches. In
this paper, we propose a new approach for discovering
classification rules by using gene expression
programming (GEP), a new technique of genetic
programming (GP) with linear representation. The
antecedent of discovered rules may involve many
different combinations of attributes. To guide the
search process, we suggest a fitness function
considering both the rule consistency gain and
completeness. A multiclass classification problem is
formulated as multiple two-class problems by using the
one-against-all learning method. The covering strategy
is applied to learn multiple rules if applicable for
each class. Compact rule sets are subsequently evolved
using a two-phase pruning method based on the minimum
description length (MDL) principle and the integration
theory. Our approach is also noise tolerant and able to
deal with both numeric and nominal attributes.
Experiments with several benchmark data sets have shown
up to 20% improvement in validation accuracy, compared
with C4.5 algorithms. Furthermore, the proposed GEP
approach is more efficient and tends to generate
shorter solutions compared with canonical tree-based GP
classifiers.",
- }
Genetic Programming entries for
Chi Zhou
Weimin Xiao
Thomas M Tirpak
Peter C Nelson
Citations