Abstract
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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The source codes are available from the corresponding author on reasonable request.
Data availability
The datasets are publicly available.
Notes
openml.org/d/299.
archive.ics.uci.edu/ml/datasets/Low+Resolution+Spectrometer.
archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits.
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This work was supported by the Slovenian Research Agency (Research Core Funding nos. P2-0057, P5-0027).
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Fister, D., Fister, I. & Karakatič, S. DynFS: dynamic genotype cutting feature selection algorithm. J Ambient Intell Human Comput 14, 16477–16490 (2023). https://doi.org/10.1007/s12652-022-03872-3
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DOI: https://doi.org/10.1007/s12652-022-03872-3