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An extensive study of statistical, rough, and hybridized rough computing in bankruptcy prediction

  • 1166: Advances of machine learning in data analytics and visual information
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Abstract

An extensive amount of data are generated from the electronic world each day. Possessing useful knowledge from this data is challenging, and it became a prime area of current research. Much research has been carried out in these directions initiating from statistical techniques to intelligent computing and further to hybridized computing. The foremost objective of this article is making a comparative study between statistical, rough computing, and hybridized computing approaches. Financial bankruptcy dataset of Polish companies is considered for comparative analysis. Results show that rough hybridization of the binary-coded genetic algorithm provides an accuracy of 98.3% and it is better as compared to other descriptive and rough computing techniques.

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Authors and Affiliations

  1. School of Computer Science and Engineering, VIT Vellore, Tamil Nadu, India

    D. P. Acharjya

  2. School of Information Technology and Engineering, VIT Vellore, Tamil Nadu, India

    R. Rathi

Authors
  1. D. P. Acharjya
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  2. R. Rathi
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Correspondence to D. P. Acharjya.

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Acharjya, D.P., Rathi, R. An extensive study of statistical, rough, and hybridized rough computing in bankruptcy prediction. Multimed Tools Appl 80, 35387–35413 (2021). https://doi.org/10.1007/s11042-020-10167-2

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  • DOI: https://doi.org/10.1007/s11042-020-10167-2

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