DynFS: dynamic genotype cutting feature selection algorithm
Journal of Ambient Intelligence and Humanized Computing(2023)
摘要
Large information datasets often impose an immense number of features where many are found redundant and thus inessential for statistical analysis. In the past, a data preprocessing phase was formalized to cope with the problem and take appropriate remedial measures. Traditionally, this was a fixed and stationary process that suffered from a lack of transparency and high susceptibility to input variations. This paper presents a novel and fully automated meta-heuristic nature-inspired wrapper-based feature selection framework DynFS with dynamically cutting search space. The experiments show that the DynFS statistically significantly overcomes a fixed feature selection framework and allows for a high level of robustness and stability.
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关键词
Feature selection,Nature-inspired algorithms,Swarm intelligence,Optimization
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