Uncertainty measures of rough set prediction

Attribute reduction in decision-theoretic rough set models

“…For example, Shannon's entropy and many of its variations have been explored to measure the uncertainty in rough set theory [3,7,15,19,28,37,39], and thus can be understood as different forms of the f measure.…”

Qualitative Spatial Representation and Reasoning

“…It is worth noting the similarity of these ideas to rough sets [60], though the exact relationship has yet to be fully explored, though see, for example, [154,20]. Other approaches to spatial uncertainty are to work with an indistinguishability relation which is not transitive and thus fails to generate equivalence classes [199,118], and the development of nonmonotonic spatial logics [188,3].…”

A Rough Set Approach for the Discovery of Classification Rules in Interval-Valued Information Systems

“…This theory is an extension of classical set theory for the study of systems characterized by insufficient and incomplete information, and has been demonstrated to be useful in fields such as pattern recognition, machine learning, and automated knowledge acquisition [14,27,[30][31][32]46,48]. Rough-set data analysis uses only internal knowledge, avoids external parameters, and does not rely on prior model assumptions such as probabilistic distribution in statistical methods, membership function in fuzzy sets theory, and basic probability assignment in Dempster-Shafer theory of evidence [7,33]. Its basic idea is to unravel an optimal set of decision rules from an information system (basically a feature-value table) via an objective knowledge induction process which determines the necessary and sufficient features constituting the rules for classification.…”