Archive-based multiple feature construction methods using adaptive Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7906
- @InProceedings{jia:2024:GECCOcomp2,
-
author = "Kaixuan Jia and Jianbin Ma and Xiaoying Gao and
Jiaxin Niu",
-
title = "Archive-based multiple feature construction methods
using adaptive Genetic Programming",
-
booktitle = "Proceedings of the 2024 Genetic and Evolutionary
Computation Conference Companion",
-
year = "2024",
-
editor = "Ting Hu and Aniko Ekart",
-
pages = "503--506",
-
address = "Melbourne, Australia",
-
series = "GECCO '24",
-
month = "14-18 " # jul,
-
organisation = "SIGEVO",
-
publisher = "Association for Computing Machinery",
-
publisher_address = "New York, NY, USA",
-
keywords = "genetic algorithms, genetic programming, feature
construction, adaptive, multiple feature,
classification: Poster",
-
isbn13 = "979-8-4007-0495-6",
-
DOI = "doi:10.1145/3638530.3654239",
-
size = "4 pages",
-
abstract = "The quality of features is an important factor that
affects the classification performance of machine
learning algorithms. Feature construction based on
Genetic Programming (GP) can automatically create more
discriminative features, sometimes greatly improving
classification performance. However, insufficient
information caused by constructing a single feature or
constructing only a few features can affect the
classification performance of feature construction. In
addition, GP may fall into premature convergence, which
also affects classification performance. This paper
proposes an archive-based multiple feature construction
method which uses elite archive strategy to preserve
and select effective constructed features, and employs
an adaptive strategy for GP to adjust the crossover and
mutation probabilities based on fitness values.
Experiments on ten datasets show that our proposed
archive-based multiple feature construction method
without using adaptive GP can significantly improve the
classification performance compared with traditional
single feature construction method, and the
classification performance can be maintained or further
improved by adding the adaptive strategy. The
comparisons with three state-of-the-art techniques show
that our proposed methods can significantly achieve
better classification performance.",
-
notes = "GECCO-2024 GP A Recombination of the 33rd
International Conference on Genetic Algorithms (ICGA)
and the 29th Annual Genetic Programming Conference
(GP)",
- }
Genetic Programming entries for
Kaixuan Jia
Jianbin Ma
Xiaoying (Sharon) Gao
Jiaxin Niu
Citations