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Feature selection in high-dimensional data: an enhanced RIME optimization with information entropy pruning and DBSCAN clustering

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Abstract

When confronted with high-dimensional data, evolutionary feature selection methods encounter the formidable challenge known as the “curse of dimensionality”. To overcome this challenge, our study delves into developing an optimized algorithm, enhanced RIME (ERIME), which ingeniously integrates feature information entropy pruning and the DBSCAN spatial clustering particle evolution method to tackle the intricacies of feature selection. Initially, a dimensionality reduction strategy is introduced based on feature information entropy pruning, effectively limiting the computational burden associated with high-dimensional data. This strategy enhances the algorithm’s search speed and substantially boosts its overall efficiency. Hence, a particle deletion strategy is proposed to safeguard the quality of particles within the population, selectively eliminating particles through multidimensional clustering while monitoring individual fitness differences. Additionally, we incorporate a particle generation strategy rooted in the Markov chain Monte Carlo method, strategically sampling and generating new particles from the distribution of superior particles. The efficacy of the ERIME is rigorously evaluated on 26 benchmark datasets, including 14 high-dimensional datasets and 12 low-dimensional datasets. A comprehensive comparison is conducted with seven cutting-edge intelligent algorithms: IMFO, PSO_GWO, MCGSA, BBPS, QBBA, SSA, and WOA. The experimental results demonstrate the algorithm’s ability to obtain exceptional feature subsets, notably showcasing its competitive edge in resolving high-dimensional feature selection challenges. Regarding average fitness value, ERIME attains a remarkable 79.54% improvement in convergence accuracy compared to RIME. Regarding the average classification error rate, ERIME achieves an impressive 98.54% reduction compared to RIME. Furthermore, in effectively selecting features, ERIME outperforms RIME by reducing the count by 85%. In conclusion, the enhanced RIME algorithm proposed in this study effectively solves the complex problems of high-dimensional feature selection.

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Acknowledgements

This work was supported in part by the Natural Science Foundation of Zhejiang Province (LTGS23E070001, LZ22F020005), National Natural Science Foundation of China (62076185, 62301367, 52204263).

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Authors and Affiliations

  1. Institute of Big data and Information Technology, Wenzhou University, Wenzhou, 325035, China

    Huangying Wu, Yi Chen, Zhennao Cai & Huiling Chen

  2. School of Resources and Safety Engineering, Central South University, Changsha, 410083, China

    Wei Zhu

  3. School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Ali Asghar Heidari

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  1. Huangying Wu
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Contributions

Huangying Wu: contributions: writing—original draft, writing—review and editing, software, visualization, investigation. Yi Chen: contributions: conceptualization, methodology, formal analysis, investigation, writing—review and editing, funding acquisition, supervision. Wei Zhu: contributions: writing—original draft, writing—review and editing, software, visualization, investigation. Zhennao Cai: contributions: writing—original draft, writing—review and editing, software, visualization, investigation. Ali Asghar Heidari: contributions: writing—original draft, writing—review and editing, software, visualization, investigation. Huiling Chen: contributions: conceptualization, methodology, formal analysis, investigation, writing—review and editing, funding acquisition, supervision.

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Appendix

Appendix

See Tables 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 and 24.

Table 6 Comparison of the developed ERIME with other binary meta-heuristic algorithms in terms of average fitness values for high-dimensional datasets
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Table 7 Comparison of the developed ERIME with other binary meta-heuristic algorithms in terms of average fitness values for complementary low-dimensional datasets
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Table 8 Comparison of the developed ERIME with other binary meta-heuristic algorithms in terms of error values for high-dimensional dataset
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Table 9 Comparison of the developed ERIME with other binary meta-heuristic algorithms in terms of error values for complementary low-dimensional datasets
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Table 10 Comparison between the developed ERIME and other binary metaheuristic algorithms on average of selected features for high-dimensional dataset
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Table 11 Comparison between the developed ERIME and other binary metaheuristic algorithms on average of selected features for low-dimensional dataset
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Table 12 Comparative experiments of ERIME on high-dimensional datasets (average fitness values)
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Table 13 Comparative experiments of ERIME on high-dimensional datasets (error rate)
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Table 14 Comparative experiments of ERIME on high-dimensional datasets (selected features)
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Table 15 ERIME parameter average fitness value test on 14 high-dimensional data sets
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Table 16 ERIME parameter average error test on 14 high-dimensional data sets
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Table 17 ERIME parameter average fitness value test on 14 high-dimensional data sets
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Table 18 ERIME parameter average fitness value test on 14 high-dimensional data sets
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Table 19 ERIME parameter average error test on 14 high-dimensional data sets
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Table 20 ERIME parameter of selected features test on 14 high-dimensional data sets
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Table 21 ERIME parameter average fitness value test on 14 high-dimensional data sets
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Table 22 ERIME parameter average error test on 14 high-dimensional data sets
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Table 23 ERIME parameter selected features test on 14 high-dimensional data sets
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Table 24 Comparative experiments of ERIME on high-dimensional datasets (time)
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Wu, H., Chen, Y., Zhu, W. et al. Feature selection in high-dimensional data: an enhanced RIME optimization with information entropy pruning and DBSCAN clustering. Int. J. Mach. Learn. & Cyber. 15, 4211–4254 (2024). https://doi.org/10.1007/s13042-024-02143-1

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