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A Comparative Study of Statistical and Rough Computing Models in Predictive Data Analysis

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  • Debi Acharjya

    (School of Computing Science and Engineering, VIT University, Vellore, India)

  • A. Anitha

    (School of Information Technology and Engineering, VIT University, Vellore, India)

Abstract

Information and technology revolution has brought a radical change in the way data are collected. The data collected is of no use unless some useful information is derived from it. Therefore, it is essential to think of some predictive analysis for analyzing data and to get meaningful information. Much research has been carried out in the direction of predictive data analysis starting from statistical techniques to intelligent computing techniques and further to hybridize computing techniques. The prime objective of this paper is to make a comparative analysis between statistical, rough computing, and hybridized techniques. The comparative analysis is carried out over financial bankruptcy data set of Greek industrial bank ETEVA. It is concluded that rough computing techniques provide better accuracy 88.2% as compared to statistical techniques whereas hybridized computing techniques provides still better accuracy 94.1% as compared to rough computing techniques.

Suggested Citation

  • Debi Acharjya & A. Anitha, 2017. "A Comparative Study of Statistical and Rough Computing Models in Predictive Data Analysis," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 8(2), pages 32-51, April.
    • Handle: RePEc:igg:jaci00:v:8:y:2017:i:2:p:32-51
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    Cited by:

    1. Soumi Majumder & Debasish Biswas, 2023. "A Bibliometric and Co-Occurrence Analysis of Work-Life Balance: Related Literature Published Pre- and During COVID-19 Pandemic," International Journal of Information Systems and Supply Chain Management (IJISSCM), IGI Global, vol. 16(1), pages 1-11, January.
    2. Sankhadeep Chatterjee & Sarbartha Sarkar & Nilanjan Dey & Soumya Sen, 2018. "Non-Dominated Sorting Genetic Algorithm-II-Induced Neural-Supported Prediction of Water Quality with Stability Analysis," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 1-20, June.

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