Hierarchical neighborhood entropy based multi-granularity attribute reduction with application to gene prioritization
As a prominent model of granular computing, neighborhood rough set provides clear
granularity organization and expression in terms of inherent parameter (neighborhood …
granularity organization and expression in terms of inherent parameter (neighborhood …
Accelerated multi-granularity reduction based on neighborhood rough sets
The notion of multi-granularity has been introduced into various mathematical models in
granular computing. For example, neighborhood rough sets can derive a good multi …
granular computing. For example, neighborhood rough sets can derive a good multi …
Attribute reduction with personalized information granularity of nearest mutual neighbors
Neighborhood-based attribute reduction plays a vital role in pattern recognition, for selecting
a series of informative and relevant attributes from data sets. The increase in dimensionality …
a series of informative and relevant attributes from data sets. The increase in dimensionality …
Mapreduce accelerated attribute reduction based on neighborhood entropy with apache spark
C Luo, Q Cao, T Li, H Chen, S Wang - Expert Systems with Applications, 2023 - Elsevier
Attribute reduction is nowadays an extremely important data preprocessing technique in the
field of data mining, which has gained much attention due to its ability to provide better …
field of data mining, which has gained much attention due to its ability to provide better …
Parameterized maximum-entropy-based three-way approximate attribute reduction
Three-way decision theory has emerged as an effective method for attribute reduction when
dealing with vague, uncertain, or imprecise data. However, most existing attribute reduction …
dealing with vague, uncertain, or imprecise data. However, most existing attribute reduction …
An efficient selector for multi-granularity attribute reduction
Presently, the mechanism of multi-granularity has been frequently realized by various
mathematical tools in Granular Computing especially rough set. Nevertheless, as a key topic …
mathematical tools in Granular Computing especially rough set. Nevertheless, as a key topic …
Optimal granularity selection based on algorithm stability with application to attribute reduction in rough set theory
Y Gao, D Chen, H Wang - Information Sciences, 2024 - Elsevier
Optimal granularity selection is a key issue in rough set, by which decision rules can be
generated to assign corresponding decision labels for new samples. The current evaluation …
generated to assign corresponding decision labels for new samples. The current evaluation …
[HTML][HTML] Cost-sensitive approximate attribute reduction with three-way decisions
In the research spectrum of rough set, the task of attribute reduction is obtaining a minimal
attribute subset that preserves certain properties of the original data. Cost-sensitive attribute …
attribute subset that preserves certain properties of the original data. Cost-sensitive attribute …
Neighborhood multi-granulation rough sets-based attribute reduction using Lebesgue and entropy measures in incomplete neighborhood decision systems
L Sun, L Wang, W Ding, Y Qian, J Xu - Knowledge-Based Systems, 2020 - Elsevier
For incomplete data with mixed numerical and symbolic attributes, attribute reduction based
on neighborhood multi-granulation rough sets (NMRS) is an important method to improve …
on neighborhood multi-granulation rough sets (NMRS) is an important method to improve …
Data-guided multi-granularity selector for attribute reduction
Z Jiang, H Dou, J Song, P Wang, X Yang, Y Qian - Applied Intelligence, 2021 - Springer
Presently, the greedy searching strategy has been widely accepted for obtaining reduct in
the field of rough set. In the framework of greedy searching, the evaluation of the candidate …
the field of rough set. In the framework of greedy searching, the evaluation of the candidate …