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Fuzzy rough assisted missing value imputation and feature selection

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Abstract

Presence of missing values and irrelevant features are commonplace issues that need to be handled effectively. Missing value imputation and feature selection is an efficient technique for redressing such problems. Fuzzy rough set-based approaches provide a handful of solutions for further dealing with vagueness and uncertainty available in the data. The present paper introduces the notion of imputing missing values followed by feature selection utilizing fuzzy rough set-based approaches. The idea of missing value estimation and instance ignorance are combined for fuzzy rough missing value imputation employing only correlated features followed by feature selection with a search heuristic. The experimental evaluation on benchmark datasets demonstrates the applicability and robustness of the proposed work. It significantly reduces data dimensionality after imputing missing values maintaining high performances. A comparative analysis demonstrates the superiority of the proposed methodology.

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Data Availability

All data analysed during this study are from UCI [30], open ML [31] and open MV net (https://openmv.net/tag/missing-data).

Notes

  1. https://openmv.net/tag/missing-data.

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Acknowledgements

This research work is funded by UGC Research Fellowship, India (Grant no: 3600/(PWD)(NET-NOV2017)) awarded to first author.

Funding

This study was funded by UGC Research Fellowship, India.

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Authors and Affiliations

  1. Department of Mathematical Sciences, Indian Institute of Technology, Banaras Hindu University, Varanasi, Uttar Pradesh, 221005, India

    Pankhuri Jain & Tanmoy Som

  2. Department of Computer Science and Information Technology, Central University of Haryana, Mahendragarh, India

    Anoop Tiwari

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  1. Pankhuri Jain
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  2. Anoop Tiwari
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Correspondence to Pankhuri Jain.

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Jain, P., Tiwari, A. & Som, T. Fuzzy rough assisted missing value imputation and feature selection. Neural Comput & Applic 35, 2773–2793 (2023). https://doi.org/10.1007/s00521-022-07754-9

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