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AE-MCDM: an autoencoder-based multi-criteria decision-making approach for unsupervised feature selection

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Abstract

Feature selection is a fundamental technique for reducing the dimensionality of high-dimensional data by identifying the most relevant features while discarding redundant or irrelevant ones. In unsupervised settings, where labeled data are unavailable and labeling is costly, effective feature selection becomes even more challenging. This paper proposes AE-MCDM, a novel unsupervised feature selection method that integrates autoencoder-based feature extraction with multi-criteria decision-making (MCDM). The autoencoder captures high-level feature representations, and the connection weights between input features and hidden neurons reflect feature importance. These weights are then processed using MCDM to rank and select the most informative features. Unlike conventional unsupervised feature selection methods, AE-MCDM leverages deep representation learning to enhance feature evaluation. To the best of our knowledge, this is the first attempt to combine autoencoders with MCDM for feature selection. Extensive experiments on various datasets demonstrate that AE-MCDM outperforms existing methods in terms of clustering performance, measured by metrics such as accuracy, precision, recall, and normalized mutual information (NMI), while also achieving competitive computational efficiency.

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Data availability

Data generated during the study are subject to a data sharing mandate and available in a few public repositories. All used data are cited in text.

Notes

  1. https://jundongl.github.io/scikit-feature/datasets.html.

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Funding

This research did not receive any specific grant from public, commercial, or not-for-profit funding agencies.

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Lorestan University, Khorramabad, Iran

    Amin Hashemi

  2. Department of Computer Engineering, Faculty of Engineering, Lorestan University, Khorramabad, Iran

    Mohammad Bagher Dowlatshahi

  3. Informal Technology Group, Wageningen University & Research, Wageningen, Netherlands

    Siamak Farshidi

  4. School of Engineering, RMIT University, Melbourne, Australia

    Parham Moradi

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  1. Amin Hashemi
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  2. Mohammad Bagher Dowlatshahi
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  4. Parham Moradi
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Contributions

Amin Hashemi, Mohammad Bagher Dowlatshahi, Parham Moradi, and Siamak Farshidi proposed the research idea, then Amin Hashemi implemented the experiments, and finally, Amin Hashemi and Mohammad Bagher Dowlatshahi wrote the manuscript. All authors discussed the results and contributed to the final manuscript. All authors reviewed the manuscript.

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Correspondence to Mohammad Bagher Dowlatshahi.

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Hashemi, A., Dowlatshahi, M.B., Farshidi, S. et al. AE-MCDM: an autoencoder-based multi-criteria decision-making approach for unsupervised feature selection. J Supercomput 81, 804 (2025). https://doi.org/10.1007/s11227-025-07316-5

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