Hybrid adapted fast correlation FCBF-support vector machine recursive feature elimination for feature selection
| Autor: | Souad Guessoum, Hayet Djellali, Nacira Ghoualmi-Zine |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science Pattern recognition Feature selection 02 engineering and technology Human-Computer Interaction Correlation Support vector machine ComputingMethodologies_PATTERNRECOGNITION Artificial Intelligence Feature (computer vision) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business Software |
| Zdroj: | Intelligent Decision Technologies. 14:269-279 |
| ISSN: | 1875-8843 1872-4981 |
| DOI: | 10.3233/idt-190014 |
| Popis: | This paper investigates feature selection methods based on hybrid architecture using feature selection algorithm called Adapted Fast Correlation Based Feature selection and Support Vector Machine Recursive Feature Elimination (AFCBF-SVMRFE). The AFCBF-SVMRFE has three stages and composed of SVMRFE embedded method with Correlation based Features Selection. The first stage is the relevance analysis, the second one is a redundancy analysis, and the third stage is a performance evaluation and features restoration stage. Experiments show that the proposed method tested on different classifiers: Support Vector Machine SVM and K nearest neighbors KNN provide a best accuracy on various dataset. The SVM classifier outperforms KNN classifier on these data. The AFCBF-SVMRFE outperforms FCBF multivariate filter, SVMRFE, Particle swarm optimization PSO and Artificial bees colony ABC. |
| Databáze: | OpenAIRE |
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