Genetic Programming for Feature Selection and Construction to High-Dimensional Data
Created by W.Langdon from
gp-bibliography.bib Revision:1.8344
- @InProceedings{Ma:2024:MLISE,
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author = "Jianbin Ma and Man Zhu",
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title = "Genetic Programming for Feature Selection and
Construction to High-Dimensional Data",
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booktitle = "2024 4th International Conference on Machine Learning
and Intelligent Systems Engineering (MLISE)",
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year = "2024",
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pages = "196--200",
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month = jun,
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keywords = "genetic algorithms, genetic programming, Feature
extraction, Decision trees, Intelligent systems,
Feature Construction, Feature Selection,
High-dimensional, Classification",
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DOI = "
doi:10.1109/MLISE62164.2024.10674630",
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abstract = "Classification on high-dimensional data is a
challenging task due to a large number of redundant and
irrelevant features. Feature construction using Genetic
Programming (GP) is an effective feature processing
approach for classification, however, in
high-dimensional applications, too many redundant and
irrelevant features may reduce GP's search ability and
affect the classification performance of feature
construction. In this paper, two feature selection and
construction approaches to high-dimensional data are
proposed. The first is a two-stage feature selection
and construction approach named LfsFc, which first uses
linear forword feature selection method (Lfs) to reduce
the search space of features, and then uses a GP-based
multiple feature construction approach (Fc) to
construct multiple features. The second is a
multi-objective GP-based feature selection and
construction approach named MoFc which optimises
information gain ratio and the number of selected
features, and archives elite individuals as constructed
features. Experiments on ten high-dimensional datasets
show that LfsFc and MoFc can improve the classification
performance compared with Fc and original features in
four decision tree classifiers.",
-
notes = "Also known as \cite{10674630}",
- }
Genetic Programming entries for
Jianbin Ma
Man Zhu
Citations