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MBSSA-Bi-AESN: Classification prediction of bi-directional adaptive echo state network based on modified binary salp swarm algorithm and feature selection

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Abstract

In the era of big data, the demand for multivariate time series prediction has surged, drawing increased attention to feature selection and neural networks in machine learning. However, certain feature selection methods neglect the alignment between actual data sample differences and clustering results, while neural networks lack automatic parameter adjustment in response to changing target features. This paper presents the MBSSA-Bi-AESN model, a Bi-directional Adaptive Echo State Network that utilizes the modified salp swarm algorithm (MBSSA) and feature selection to address the limitations of manually set parameters. Initial feature subset selection involves assigning weights based on the consistency of clustering results with differences. Subsequently, the four critical parameters in the Bi-AESN model are optimized using MBSSA. The optimized Bi-AESN model and selected feature subset are then integrated for simultaneous model learning and optimal feature subset selection. Experimental analysis on eight datasets demonstrates the superior prediction accuracy of the MBSSA-Bi-AESN model compared to benchmark models, underscoring its feasibility, validity, and universality.

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Notes

  1. https://archive.ics.uci.edu/ml/datasets

  2. https://sci2s.ugr.es/keel/datasets

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Acknowledgements

The authors are extremely grateful to the editors and five anonymous referees for their valuable comments which helped us improve the presentation of this article.

Funding

The present work was in part supported by grants from the NNSFC (12271146; 12161036; 62176221; 61976120).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Hubei Minzu University, Enshi, Hubei, 445000, China

    Xunjin Wu & Jianming Zhan

  2. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 611756, China

    Tianrui Li

  3. School of Information Science and Technology, Nantong University, Nangtong, 226019, China

    Weiping Ding

  4. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2R3, Canada

    Witold Pedrycz

  5. Systems Research Institute, Polish Academy of Sciences, Warsaw, 00-901, Poland

    Witold Pedrycz

  6. Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Sariyer/Istanbul, Turkey

    Witold Pedrycz

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  1. Xunjin Wu
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Contributions

Xunjin Wu: Conceptualization, Methodology, Investigation, Writing-original draft. Jianming Zhan: Methodology, Investigation, Writing-original draft. Tianrui Li: Methodology, Writing-Reviewing and Editing. Weiping Ding: Methodology, Writing-Reviewing and Editing. Witold Pedrycz: Writing-Reviewing and Editing.

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Correspondence to Jianming Zhan.

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Wu, X., Zhan, J., Li, T. et al. MBSSA-Bi-AESN: Classification prediction of bi-directional adaptive echo state network based on modified binary salp swarm algorithm and feature selection. Appl Intell 54, 1706–1733 (2024). https://doi.org/10.1007/s10489-024-05280-w

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