Feature selection based on brain storm optimization for data classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @Article{POURPANAH:2019:ASC,
-
author = "Farhad Pourpanah and Yuhui Shi and Chee Peng Lim and
Qi Hao and Choo Jun Tan",
-
title = "Feature selection based on brain storm optimization
for data classification",
-
journal = "Applied Soft Computing",
-
volume = "80",
-
pages = "761--775",
-
year = "2019",
-
ISSN = "1568-4946",
-
DOI = "doi:10.1016/j.asoc.2019.04.037",
-
URL = "http://www.sciencedirect.com/science/article/pii/S1568494619302297",
-
keywords = "genetic algorithms, genetic programming, Feature
selection, Brain storm optimization, Fuzzy ARTMAP, Data
classification",
-
abstract = "Brain storm optimization (BSO) is a new and effective
swarm intelligence method inspired by the human
brainstorming process. This paper presents a novel
BSO-based feature selection technique for data
classification. Specifically, the Fuzzy ARTMAP (FAM)
model, which is employed as an incremental learning
neural network, is combined with BSO, which acts as a
feature selection method, to produce the hybrid FAM-BSO
model for feature selection and optimization. Firstly,
FAM is used to create a number of prototype nodes
incrementally. Then, BSO is used to search and select
an optimal sub-set of features that is able to produce
high accuracy with the minimum number of features. Ten
benchmark problems and a real-world case study are
employed to evaluate the performance of FAM-BSO. The
results are quantified statistically using the
bootstrap method with the 95percent confidence
intervals. The outcome indicates that FAM-BSO is able
to produce promising results as compared with those
from original FAM and other feature selection methods
including particle swarm optimization, genetic
algorithm, genetic programming, and ant colony
optimization",
- }
Genetic Programming entries for
Farhad Pourpanah
Yuhui Shi
Chee Peng Lim
Qi Hao
Choo Jun Tan
Citations