Zakeri, A and Hokmabadi, A (2019) Efficient feature selection method using real-valued grasshopper optimization algorithm. Expert Systems with Applications, 119. pp. 61-72. ISSN 0957-4174
Abstract
Feature selection is the problem of finding the minimum number of features among a redundant feature space which leads to the maximum classification performance. In this paper, we have proposed a novel feature selection method based on mathematical model of interaction between grasshoppers in finding food sources. Some modifications were applied to the grasshopper optimization algorithm (GOA) to make it suitable for a feature selection problem. The method, abbreviated as GOFS is supplemented by statistical measures during iterations to replace the duplicate features with the most promising features. Several publicly available datasets with various dimensionalities, number of instances, and target classes were considered to evaluate the performance of the GOFS algorithm. The results of implementing twelve well-known and recent feature selection methods were presented and compared with GOFS algorithm. Comparative experiments indicate the significance of the proposed method in comparison with other feature selection methods.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | Feature selection; Grasshopper optimization algorithm; Meta-heuristic algorithms; Pattern recognition |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Aug 2020 11:12 |
Last Modified: | 07 Aug 2020 14:02 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.eswa.2018.10.021 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:164089 |