A rough set approach to feature selection based on ant colony optimization
By Yuming Chen, Duoqian Miao, Ruizhi Wang. Pattern Recognition Letters, 2010
302 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computational Theory and Mathematics, Information Systems
View Paper: https://www.semanticscholar.org/paper/affb4f9f270f955e8871644d2b1f78413263e0cf
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In Computational Mechanics
In Computational Theory and Mathematics
In Information Systems
Citations in the corpus
- Feature selection for high-dimensional classification using a competitive swarm optimizer (2016, methodology): “Chen et al. (2010) have applied ant colony optimization (ACO) together with rough set theory for feature selection.”
- Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms (2014, methodology): “[25] also successfully use ACO and rough set theory to solve feature selection problems.”
- Feature selection in machine learning: A new perspective (2018, background): “According to the theoretical principle, feature selection methods can be based on statistics [35-39], information theory [40-45], manifold [46-48], and rough set [49-53], and can be”