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MapReduce based parallel fuzzy-rough attribute reduction using discernibility matrix

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Abstract

Fuzzy-rough set theory is an efficient method for attribute reduction. It can effectively handle the imprecision and uncertainty of the data in the attribute reduction. Despite its efficacy, current approaches to fuzzy-rough attribute reduction are not efficient for the processing of large data sets due to the requirement of higher space complexities. A limited number of accelerators and parallel/distributed approaches have been proposed for fuzzy-rough attribute reduction in large data sets. However, all of these approaches are dependency measure based methods in which fuzzy similarity matrices are used for performing attribute reduction. Alternative discernibility matrix based attribute reduction methods are found to have less space requirements and more amicable to parallelization in building parallel/distributed algorithms. This paper therefore introduces a fuzzy discernibility matrix-based attribute reduction accelerator (DARA) to accelerate the attribute reduction. DARA is used to build a sequential approach and the corresponding parallel/distributed approach for attribute reduction in large data sets. The proposed approaches are compared to the existing state-of-the-art approaches with a systematic experimental analysis to assess computational efficiency. The experimental study, along with theoretical validation, shows that the proposed approaches are effective and perform better than the current approaches.

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Data and material are available with the authors.

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Acknowledgments

Authors are grateful to the reviewers for their valuable comments and suggestions. This work is supported by the Department of Science and Technology (DST), the Government of India under the ICPS project [grant number: DST/ICPS/CPS-Individual/2018/579(G) and DST/ICPS/CPS-Individual/2018/579(C)], and by the Digital India Corporation of the Ministry of Electronics and Information Technology, the Government of India under the Visvesvaraya PhD. scheme with the unique id: MEITY-PHD-1039. We would like to extend our sincere thanks to the authors of the PARA [30] algorithm for providing the source code.

Funding

This work is supported by Department of Science and Technology (DST), Government of India under ICPS project [grant number: DST/ICPS/CPS-Individual/2018/579(G) and DST/ICPS/CPS-Individual/2018/579(C)], and by Digital India Corporation, a Section 8 Company of Ministry of Electronics and Information Technology, Government of India under Visvesvaraya Ph.D. scheme with the unique id: MEITY-PHD-1039.

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Authors and Affiliations

  1. School of Computer and Information Sciences, University of Hyderabad, Hyderabad, 500046, Telangana, India

    Pandu Sowkuntla & P. S. V. S. Sai Prasad

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  1. Pandu Sowkuntla
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Correspondence to P. S. V. S. Sai Prasad.

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Sowkuntla, P., Prasad, P.S.V.S.S. MapReduce based parallel fuzzy-rough attribute reduction using discernibility matrix. Appl Intell 52, 154–173 (2022). https://doi.org/10.1007/s10489-021-02253-1

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