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Approaches for coarsest granularity based near-optimal reduct computation

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Abstract

Traditionally, the shortest length has been used as the optimality criterion in rough set based optimal / near-optimal reduct computation. A more generalizable alternative to the optimal reduct computation approach was recently introduced, with the coarsest granular space as the optimality criterion. However, owing to exponential time complexity, it is not scalable to even moderate-sized data sets. This article investigates to formulate two near-optimal reduct computation alternatives for scaling comparatively larger data sets. The first algorithm employs a controlled A∗ search based strategy to find a near-optimal reduct while reducing both space utilization and computational time. Whereas, the second algorithm employs a greedy sequential backward elimination (SBE) strategy on the higher granular space attribute ordering for achieving coarsest granular space based near-optimal reduct. The comparative experimental study is conducted among the proposed approaches with the coarsest granular space based optimal reduct algorithm A∗RSOR and state-of-the-art shortest length based optimal and near-optimal reduct algorithms. The experimental study amply validates the relevance of the proposed approaches in obtaining near-optimal reduct with increased scalability and comparable or improved generalizable classification models induction.

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Notes

  1. The granular space having the smallest cardinality is considered as coarsest granular space.

  2. A simplified binary discernibility matrix obtained through repeated applications of absorption law

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Acknowledgements

This research acknowledges the financial support of UoH-IoE by MHRD (F11/9/2019-U3(A)). The first author acknowledges the support of the Senior Research Fellowship from the Council of Scientific & Industrial Research (CSIR), Ministry of Science and Technology, Government of India.

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

    Abhimanyu Bar & P. S. V. S. Sai Prasad

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  1. Abhimanyu Bar
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Bar, A., Prasad, P.S.V.S.S. Approaches for coarsest granularity based near-optimal reduct computation. Appl Intell 53, 4231–4256 (2023). https://doi.org/10.1007/s10489-022-03571-8

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