Attribute reduction methods in fuzzy rough set theory: An overview, comparative experiments, and new directions
Z Yuan, H Chen, P ** and selection with graph theory in robust fuzzy rough approximation space
Most extant feature selection works neglect interactive features in the form of groups, leading
to the omission of some important discriminative information. Moreover, the prevalence of …
to the omission of some important discriminative information. Moreover, the prevalence of …
Instance and feature selection using fuzzy rough sets: a bi-selection approach for data reduction
X Zhang, C Mei, J Li, Y Yang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Data reduction, aiming to reduce the original data by selecting the most representative
information, is an important technique of preprocessing data. At present, large-scale or huge …
information, is an important technique of preprocessing data. At present, large-scale or huge …
Noise-Tolerant Fuzzy--Covering-Based Multigranulation Rough Sets and Feature Subset Selection
Z Huang, J Li, Y Qian - IEEE Transactions on Fuzzy Systems, 2021 - ieeexplore.ieee.org
As a novel fuzzy covering, fuzzy covering has attracted considerable attention. However, the
traditional fuzzy--covering-based rough set and most of its extended models cannot well fit …
traditional fuzzy--covering-based rough set and most of its extended models cannot well fit …
Fuzzy information entropy-based adaptive approach for hybrid feature outlier detection
Fuzzy information entropy based on fuzzy relation in fuzzy rough set theory is an important
metric of uncertainty. However, the research of fuzzy information entropy for hybrid feature …
metric of uncertainty. However, the research of fuzzy information entropy for hybrid feature …