Enhancing big data feature selection using a hybrid correlation-based feature selection

Enhancing big data feature selection using a hybrid correlation-based feature selection

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This study proposes an alternate data extraction method that combines three well-known feature selection methods for handling large and problematic datasets: the correlation-based feature selection (CFS), best first search (BFS), and dominance-based rough set approach (DRSA) methods. This study aims...

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Bibliographic Details
Main Authors: Crespo, R.G (Author), Fujita, H. (Author), Herrera-Viedma, E. (Author), Krejcar, O. (Author), Mohamad, M. (Author), Selamat, A. (Author)
Format: Article
Language:English
Published: MDPI 2021
Series:Electronics (Switzerland)
Subjects:
Big data
Correlation-based feature selection
Deep learning
DRSA
Feature selection
Neural network
Support vector machines (SVM)
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