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Uncertainty optimization based feature subset selection model using rough set and uncertainty theory

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Abstract

The rough set is a tool for the assessment of uncertainty, and the rough set reducts formation is the technique to remove uncertainty in the feature set for feature subset selection. This work uses uncertainty theory from the rough set perspective to find uncertainty optimization-based reducts (UOR). We formulate an algorithm based on uncertainty optimization to obtain reducts of the feature set for effectiveness and performance enhancement in feature selection. The average accuracy of the reducts found by the UOR algorithm is up to 96.66%. The proposed reduct approach is compared with the existing methods using the same numerical datasets. The comparison results show that the UOR method finds feature subsets of minimum sizes with similar classification accuracy compared to existing reduct methods.

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Acknowledgements

The authors are very grateful to the Department of Mathematics, National Institute of Technology Raipur (C. G.), India, for giving facilities, space, and an opportunity for the work.

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  1. Pradeep Shende and Nishant Namdev have contributed equally to this work.

Authors and Affiliations

  1. Department of Mathematics, National Institute of Technology Raipur, Chhattisgarh, India

    Arvind Kumar Sinha, Pradeep Shende & Nishant Namdev

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  1. Arvind Kumar Sinha
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  2. Pradeep Shende
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Correspondence to Arvind Kumar Sinha.

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Sinha, A.K., Shende, P. & Namdev, N. Uncertainty optimization based feature subset selection model using rough set and uncertainty theory. Int. j. inf. tecnol. 14, 2723–2739 (2022). https://doi.org/10.1007/s41870-022-00994-x

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