An Attribute Reduction Method Using Neighborhood Entropy Measures in Neighborhood Rough Sets [PDF]
Entropy, 2019 Attribute reduction as an important preprocessing step for data mining, and has become a hot research topic in rough set theory. Neighborhood rough set theory can overcome the shortcoming that classical rough set theory may lose some useful information ...
Lin Sun +3 more
doaj +7 more sources
Feature selection based on neighborhood rough sets and Gini index [PDF]
PeerJ Computer Science, 2023 Neighborhood rough set is considered an essential approach for dealing with incomplete data and inexact knowledge representation, and it has been widely applied in feature selection.
Yuchao Zhang +8 more
doaj +3 more sources
A Neighborhood Rough Sets-Based Attribute Reduction Method Using Lebesgue and Entropy Measures [PDF]
Entropy, 2019 For continuous numerical data sets, neighborhood rough sets-based attribute reduction is an important step for improving classification performance. However, most of the traditional reduction algorithms can only handle finite sets, and yield low accuracy
Lin Sun +3 more
doaj +2 more sources
Reduction of Neighborhood-Based Generalized Rough Sets [PDF]
Journal of Applied Mathematics, 2011 Rough set theory is a powerful tool for dealing with uncertainty, granularity, and incompleteness of knowledge in information systems. This paper discusses five types of existing neighborhood-based generalized rough sets.
Zhaohao Wang, Lan Shu, Xiuyong Ding
doaj +4 more sources
Kernel Neighborhood Rough Sets Model and Its Application [PDF]
Complexity, 2018 Rough set theory has been successfully applied to many fields, such as data mining, pattern recognition, and machine learning. Kernel rough sets and neighborhood rough sets are two important models that differ in terms of granulation.
Kai Zeng, Siyuan Jing
doaj +2 more sources
New neighborhood based rough sets [PDF]
, 2015 Neighborhood based rough sets are important generalizations of the classical rough sets of Pawlak, as neighborhood operators generalize equivalence classes.
I Couso +11 more
core +3 more sources
Constructing three-way classifier with interval granulation neighborhood rough sets based on uncertainty invariance [PDF]
Frontiers in NeuroroboticsThree-way decision with neighborhood rough sets (3WDNRS) is effective in handling uncertain problems involving continuous data through the adjustment of the neighborhood radius. However, it faces two main limitations. Firstly, 3WDNRS relies on individual
Yongqi Wang +5 more
doaj +2 more sources
Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson’s disease behavioral analysis [PDF]
Scientific ReportsExtracting knowledge from hybrid data, comprising both categorical and numerical data, poses significant challenges due to the inherent difficulty in preserving information and practical meanings during the conversion process.
Imran Raza +6 more
doaj +2 more sources
Cost-sensitive Multigranulation Approximation of Neighborhood Rough Fuzzy Sets [PDF]
Jisuanji kexue, 2023 Multigranulation neighborhood rough sets are a new data processing mode in the theory of neighborhood rough sets,in which the target concept can be characterized by upper/lower approximate boundaries of optimistic and pessimistic,respectively ...
YANG Jie, KUANG Juncheng, WANG Guoyin, LIU Qun
doaj +1 more source
Weak Label Feature Selection Method Based on Neighborhood Rough Sets and Relief [PDF]
Jisuanji kexue, 2022 In multi-label learning and classification, existing feature selection algorithms based on neighborhood rough sets will use classification margin of samples as the neighborhood radius.However, when the margin is too large, the classification may be ...
SUN Lin, HUANG Miao-miao, XU Jiu-cheng
doaj +1 more source