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Rough set theory provides a mathematical framework for approximating subsets using lower and upper bounds defined by equivalence relations, effectively capturing uncertainty in classification and data analysis. Building upon these foundational concepts, further generalizations such as Hyperrough Sets and Superhyperrough Sets have been developed. The Weighted Rough Set is an extension of rough set theory that incorporates importance weights for attributes, enabling more precise classification and approximation. In this paper, we investigate the Weighted Hyperrough Set, Weighted Superhyperrough Set, Sequential Hyperrough Set, and Sequential 𝑛-Superhyperrough Set, each of which extends the fundamental ideas of Weighted Rough Sets and Sequential Rough Sets to more complex and high-dimensional decision environments.
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