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<p>This paper introduces Rough Set Inspired Neutrosophic Sets (RSINS), a model that combines rough sets and neutrosophic sets. Rough sets deal with incomplete data using lower and upper approximations. Neutrosophic sets describe information using three values: truth, indeterminacy, and falsity. In RSINS, lower approximations use minimum truth and maximum indeterminacy, while upper approximations use maximum truth and minimum indeterminacy. Some basic properties of the model are studied to show that it is consistent and reliable. A rough neutrosophic topological structure is also introduced. An algorithm is proposed to compute RSINS approximations. The model is applied to a decision-making problem to show its usefulness. Existing methods have some limitations. Fuzzy sets do not handle indeterminacy clearly, and rough sets do not handle partial truth. RSINS combines both ideas and overcomes these issues. The results show that the proposed method gives more consistent decisions when the data is uncertain or incomplete.</p>

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Rough set inspired neutrosophic set: a new hybrid approach for uncertain information processing

  • 期刊论文

  • 发表日期：

    2026年5月11日

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  • Arunava Bhattacharjee,
  • Sharmistha Bhattacharya Halder

摘要

This paper introduces Rough Set Inspired Neutrosophic Sets (RSINS), a model that combines rough sets and neutrosophic sets. Rough sets deal with incomplete data using lower and upper approximations. Neutrosophic sets describe information using three values: truth, indeterminacy, and falsity. In RSINS, lower approximations use minimum truth and maximum indeterminacy, while upper approximations use maximum truth and minimum indeterminacy. Some basic properties of the model are studied to show that it is consistent and reliable. A rough neutrosophic topological structure is also introduced. An algorithm is proposed to compute RSINS approximations. The model is applied to a decision-making problem to show its usefulness. Existing methods have some limitations. Fuzzy sets do not handle indeterminacy clearly, and rough sets do not handle partial truth. RSINS combines both ideas and overcomes these issues. The results show that the proposed method gives more consistent decisions when the data is uncertain or incomplete.