A structure-enhanced ordered decision framework based on weighted neighborhood rough sets and ordinal regression

Attribute Reduction with Fuzzy Weighted Neighborhood Rough Sets

Neighborhood rough sets (NRSs) have been widely recognized as an efficient tool for solving attribute reduction problems in numerical data. In constructing neighborhood information granules, many existing extensions introduce weighting mechanisms for conditional attributes to quantify their influence on the decision at...

Ordinal Selection Fuzzy Rough Sets: A Dominance Relation-Driven Feature-Instance Bidirectional Selection Method

Ordinal classification involves predicting labels with a natural ordering, yet the presence of feature redundancy and noisy instances can distort the ordinal relationships that fuzzy rough set models rely on. Most existing approaches tackle feature selection in isolation, overlooking how these two factors interact to d...

A Taylor-Polynomial Representation of Value Functions for Multi-Attribute Decision Strategies

Decision strategies in multi-attribute decision-making are described as qualitatively distinct rules with no common quantitative scale for comparing them. This study proposes a unified framework that represents a decision strategy as a feature vector built on a Taylor-polynomial value function, placing strategies in a...

Multi-attribute decision making based on neighborhood dominance relation of intuitionistic fuzzy ordered information system

Generalized Ordered Rough Set Models via Minimal and Maximal Neighborhoods Induced by Binary Relations

Classical Pawlak rough set theory is fundamentally based on equivalence relations, which limits its ability to represent uncertainty arising from non-equivalent and ordered information. This paper develops a generalized ordered rough set framework in which binary relations are combined with partial order structures to...

Multi-Attribute Decision-Making Approach with Interval-Valued T-Spherical Hesitant Fuzzy Sets

Two fundamental aggregation operators, namely the interval-valued T-spherical hesitant fuzzy weighted averaging (IVTSHF-WA) and weighted geometric (IVTSHF-WG) operators, are proposed, and the power Heronian mean is extended to the IVTSHF environment, yielding the IVTSHF-WPHM and IVTSHF-WGPHM operators.