Abstract
To achieve excellence in any business venture and have an edge over the competitors, it is necessary to optimally use the available scarce resources which form the foundation of a perpetually flourishing business enterprise. This paper employs rough set theory to categorically remove any superfluous data present in the system by establishing a discernibility matrix which accommodates the elements that differentiates the objects or the equivalence classes obtained using the indiscernibility relation. The basic principle, to achieve the objective of data reduction, is to minimize the Boolean expression obtained by logically concatenating entries of the discernibility matrix. The reduced information is subjected to the standard statistical regression procedures and is found that it is statistically consistent. Finally, an artificial neural network modeling is suggested which validates the results obtained using rough set analysis for the relations between the data variables or the given information other than the linear ones.
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Singh, A., Misra, S.C. (2020). Investigating Determinants of Profitability of Commercial Firms: Rough Set Analysis. In: Pati, B., Panigrahi, C., Buyya, R., Li, KC. (eds) Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1089. Springer, Singapore. https://doi.org/10.1007/978-981-15-1483-8_46
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DOI: https://doi.org/10.1007/978-981-15-1483-8_46
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