Skip Nav Destination
Close Modal
Update search
NARROW
Format
Journal
Date
Availability
1-1 of 1
Jing Liang
Close
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Sort by
Journal Articles
Publisher: Journals Gateway
Evolutionary Computation 1–27.
Published: 19 September 2024
Abstract
View article
PDF
Performing classification on high-dimensional data poses a significant challenge due to the huge search space. Moreover, complex feature interactions introduce an additional obstacle. The problems can be addressed by using feature selection to select relevant features or feature construction to construct a small set of high-level features. However, performing feature selection or feature construction only might make the feature set suboptimal. To remedy this problem, this study investigates the use of genetic programming for simultaneous feature selection and feature construction in addressing different classification tasks. The proposed approach is tested on 16 datasets and compared with seven methods including both feature selection and feature constructions techniques. The results show that the obtained feature sets with the constructed and/or selected features can significantly increase the classification accuracy and reduce the dimensionality of the datasets. Further analysis reveals the complementarity of the obtained features leading to the promising classification performance of the proposed method.