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@article{187433,
        author = {Arunava Bhattacharjee and Sharmistha Bhattacharya Halder and Md. Azad Hussain},
        title = {An Enhanced Reduct Method using Rough Set Theory for Attribute Reduction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {4574-4581},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187433},
        abstract = {Attribute reduction is a key task in rough set theory because it helps remove redundant attributes while keeping the information needed for reliable decision-making. This paper introduces an enhanced reduct method that aims to identify minimal and meaningful attribute subsets from decision systems. The method begins by detecting core attributes, which are essential for preserving classification ability, and then uses a guided greedy strategy to select additional attributes only when they improve decision quality. Attribute importance is measured using the dependency degree to ensure that each selected attribute contributes significantly to classification. The approach is designed to handle high-dimensional datasets and to support real-world applications such as medical diagnosis, pattern recognition, and data mining. A full evaluation and comparison with existing methods, including Quick Reduct and Greedy Heuristic techniques, will be presented in future work.},
        keywords = {Rough set theory, Attribute reduction, Reduct, Dependency degree, Feature selection.},
        month = {November},
        }