GA-Based Feature Selection Method for Imbalanced Data with Application in Radio Signal Recognition

DOI

10.1080/18756891.2015.1129577How to use a DOI?

Keywords

feature selection, genetic algorithm, imbalanced data, radio signal recognition, ground-air communication

Abstract

This paper presents an improved genetic algorithm (GA) based feature selection method for imbalanced data classification, which is then applied to radio signal recognition of ground-air communication. The proposed method improves the fitness function while SVM is selected as the classifier due to its good classification performance. This method is firstly evaluated using several benchmark datasets and experimental results show that the proposed method outperforms the original GA-based feature selection method now that it not only reduces the feature dimension effectively, but also improves the precision of the minor class. Finally, the proposed method is applied to a real world application in radio signal recognition of ground-air communication, which again shows comparatively better performance.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Limin Du
AU  - Yang Xu
AU  - Jun Liu
AU  - Fangli Ma
PY  - 2015
DA  - 2015/12/01
TI  - GA-Based Feature Selection Method for Imbalanced Data with Application in Radio Signal Recognition
JO  - International Journal of Computational Intelligence Systems
SP  - 39
EP  - 47
VL  - 8
IS  - Supplement 1
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2015.1129577
DO  - 10.1080/18756891.2015.1129577
ID  - Du2015
ER  -