Particle Swarm Optimization Algorithm for Feature Selection Inspired by Peak Ecosystem Dynamics
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Abstract
In recent years, particle swarm optimization (PSO) has received widespread attention in feature selection due to its simplicity and potential for global search. However, in traditional PSO, particles primarily update based on... | Find, read and cite all the research you need on Tech Science Press
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Research context
OpenAlex topics: Environmental Changes in China · Remote Sensing and Land Use · Advanced Algorithms and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (48)
Show source-provided references
1. Application of ReliefF algorithm to selecting feature sets for classification of high resolution remote sensing image
2. Feature extraction and selection research of sub-health recognition based on pulse wave
3. Recursive memetic algorithm for gene selection in microarray data10.1016/j.eswa.2018.06.057 ↗
4. Eye tracking data guided feature selection for image classification10.1016/j.patcog.2016.09.007 ↗
5. Brkić · Sveučilište u Zagrebu · 2013
6. Particle swarm optimization based two-stage feature selection in text mining
7. A new subset based deep feature learning method for intelligent fault diagnosis of bearing10.1016/j.eswa.2018.05.032 ↗
8. Hybrid fast unsupervised feature selection for high-dimensional data10.1016/j.eswa.2019.01.016 ↗
9. M. A. Hall, “Correlation-based feature selection for machine learning,” Ph.D. thesis, Dept. of Computer Science, Univ. of Waikato, Hamilton, New Zealand, 1998.
10. A survey on evolutionary computation approaches to feature selection10.1109/TEVC.2015.2504420 ↗
11. Improved binary particle swarm optimization using catfish effect for feature selection10.1016/j.eswa.2011.04.057 ↗
12. Chaotic maps based on binary particle swarm optimization for feature selection
13. Feature selection algorithm based on bare bones particle swarm optimization10.1016/j.neucom.2012.09.049 ↗
14. Feature selection based on hybridization of genetic algorithm and particle swarm optimization10.1109/LGRS.2014.2337320 ↗
15. A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy10.1016/j.asoc.2016.01.044 ↗
16. Hybrid particle swarm optimization with spiral-shaped mechanism for feature selection10.1016/j.eswa.2019.03.039 ↗
17. Many-objective optimization of feature selection based on two-level particle cooperation10.1016/j.ins.2020.05.004 ↗
18. Adaptive particle swarm optimization10.1109/TSMCB.2009.2015956 ↗
19. Feature selection for classification10.3233/IDA-1997-1302 ↗
20. Differential evolution based feature subset selection
21. Ant colony optimization for feature subset selection
22. An improved feature selection method based on ant colony optimization (ACO) evaluated on face recognition system
23. Genetic algorithms and machine learning10.1023/A:1022602019183 ↗
24. SemiACO: A semi-supervised feature selection based on ant colony optimization10.1016/j.eswa.2022.119130 ↗
25. A hybrid genetic algorithm and particle swarm optimization for multimodal functions10.1016/j.asoc.2007.07.002 ↗
26. Particle swarm optimization
27. A review on representative swarm intelligence algorithms for solving optimization problems: Applications and trends10.1109/JAS.2021.1004129 ↗
28. Small worlds and mega-minds: Effects of neighborhood topology on particle swarm performance
29. Improving particle swarm optimization using multi-layer searching strategy10.1016/j.ins.2014.02.143 ↗
30. A level-based learning swarm optimizer for large-scale optimization10.1109/TEVC.2017.2743016 ↗
31. Empirical study of particle swarm optimization inspired by Lotka-Volterra model in ecology10.1007/s00500-018-3215-9 ↗
32. Particle swarm optimization with moving particles on scale-free networks10.1109/TNSE.2018.2854884 ↗
33. A competitive swarm optimizer for large scale optimization10.1109/TCYB.2014.2322602 ↗
34. Particle swarm optimization with adaptive learning strategy10.1016/j.knosys.2020.105789 ↗
35. A ranking-system-based switching particle swarm optimizer with dynamic learning strategies10.1016/j.neucom.2022.04.117 ↗
36. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions10.1109/TEVC.2005.857610 ↗
37. Variable-size cooperative coevolutionary particle swarm optimization for feature selection on high-dimensional data10.1109/TEVC.2020.2968743 ↗
38. A fast hybrid feature selection based on correlation-guided clustering and particle swarm optimization for high-dimensional data10.1109/TCYB.2021.3061152 ↗
39. Information gain ratio-based subfeature grouping empowers particle swarm optimization for feature selection10.1016/j.knosys.2024.111380 ↗
40. Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces10.1023/A:1008202821328 ↗
41. A binary differential evolution algorithm learning from explored solutions10.1016/j.neucom.2014.07.030 ↗
42. A discrete binary version of the particle swarm algorithm
43. Bare bones particle swarms
44. An evolutionary feature selection method based on probability-based initialized particle swarm optimization10.1007/s13042-024-02107-5 ↗
45. Enhanced crow search algorithm for feature selection
46. Improved salp swarm algorithm based on opposition based learning and novel local search algorithm for feature selection10.1016/j.eswa.2019.113122 ↗
47. Time-varying hierarchical chains of salps with random weight networks for feature selection10.1016/j.eswa.2019.112898 ↗
48. A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection10.1016/j.eswa.2019.112824 ↗