Fast correlation based filter combined with genetic algorithm and particle swarm on feature selection
| Autor: | Nacira Ghoualmi-Zine, Hayet Djellali, Soumaya Layachi, Souad Guessoum |
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| Rok vydání: | 2017 |
| Předmět: |
Computer science
business.industry 020208 electrical & electronic engineering Feature extraction MathematicsofComputing_NUMERICALANALYSIS Particle swarm optimization Feature selection Pattern recognition 02 engineering and technology ComputingMethodologies_ARTIFICIALINTELLIGENCE Correlation Statistical classification ComputingMethodologies_PATTERNRECOGNITION Filter (video) Genetic algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | Web of Science |
| DOI: | 10.1109/icee-b.2017.8192090 |
| Popis: | This paper investigates feature selection method using filter Fast Correlation based Filter FCBF combined with Genetic Algorithm GA and particle swarm optimization PSO. In this paper two hybrid approaches based on filter method FCBF and Genetic algorithm (FCBF-GA) and filter FCBF with particle swarm (FCBF-PSO) are proposed. It has been found that the proposed method FCBF-PSO outperform the proposed FCBF-GA method and exiting methods (FCBF, GA, PSO) for classifying WDBC, colon, hepatitis, DLBCL, lung cancer dataset. Experimental results are carried out on UCI data repository and show the effectiveness of these approaches in term of accuracy and reducing the size of features. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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