Decision-theoretic rough set models

TL;DR: It is shown that, under certain conditions when considering the costs of different types of miss-classifications, probabilistic three-way decisions are superior to the other two.

TL;DR: Based on rough membership functions and rough inclusion functions, the Bayesian decision-theoretic analysis is adopted to provide a systematic method for determining the precision parameters by using more familiar notions of costs and risks.

TL;DR: Three new approaches to fuzzy-rough FS-based on fuzzy similarity relations based on crisp discernibility matrices are proposed and utilized and initial experimentation shows that the methods greatly reduce dimensionality while preserving classification accuracy.

TL;DR: A theory of three-way decisions is constructed based on the notions of acceptance, rejection and noncommitment, an extension of the commonly used binary-decision model with an added third option.

TL;DR: Theoretical Foundations.

TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.