Evolutionary Feature Selection Method via A Chaotic Binary Dragonfly Algorithm

Feature selection aims at reducing the number of attributes while achieving a high classification accuracy in machine learning. In this paper, we design a fitness function to jointly reduce the number of the selected features and enhance the accuracy. Then, we propose a chaotic binary dragonfly algo...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics s. 2471 - 2478
Hlavní autoři: Liu, Zhao, Wang, Aimin, Sun, Geng, Li, Jiahui, Bao, Haiming, Li, Hongjuan
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 09.10.2022
Témata:
chaotic map
classification
Classification algorithms
dragonfly algorithm
evolutionary population dynamics
Feature extraction
Feature selection
Heuristic algorithms
Machine learning
Machine learning algorithms
Sociology
Statistics
ISSN:2577-1655
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Feature selection aims at reducing the number of attributes while achieving a high classification accuracy in machine learning. In this paper, we design a fitness function to jointly reduce the number of the selected features and enhance the accuracy. Then, we propose a chaotic binary dragonfly algorithm (CBDA) with several improved factors on the conventional dragonfly algorithm (DA) for developing a wrapper-based feature selection method to solve the fitness function. Specifically, the CBDA introduces three improved factors that are the chaotic map, evolutionary population dynamics mechanism and binarization strategy to make the algorithm more suitable for the problem. Experiments are conducted to evaluate the performance of the proposed CBDA on 24 well-known data sets from the UCI repository, and the results demonstrate that the proposed CBDA outperforms other comparative algorithms on the majority of the tested data sets.
ISSN:2577-1655
DOI:10.1109/SMC53654.2022.9945264