Feature selection based on neighborhood rough set is a noteworthy step in dealing with
numerical data. Information entropy, proven in many theoretical analysis and practical …

In multi-label learning, since different labels may have some distinct characteristics of their
own, multi-label learning approach with label-specific features named LIFT has been …

Attribute reduction of rough set theory underlies knowledge acquisition and has two
hierarchical types (classification-based and class-specific attribute reducts) and two …

Attribute reduction is an important application of rough set theory. Most existing rough set
models do not consider the weight information of attributes in information systems. In this …

The concept of attribute reducts plays a fundamental role in rough set analysis. There are at
least two possibilities to define an attribute reduct. A classification-based or global attribute …

As is well known, soft set theory can have a bearing on making decisions in many fields.
Particularly important is parameter reduction of soft sets, an essential topic both for …

In terms of neighborhood rough sets, the tri-level granular structure of neighborhood system
(carrying the neighborhood granule, swarm, and library) establishes a granular computing …

Variable precision multigranulation fuzzy rough sets (VP-MFRSs) use two direct integrations:
the multigranulation maximum and minimum. Their optimistic and pessimistic models …

摘要大数据往往呈现出大规模性, 多模态性以及快速增长性等特征. 粒计算是智能信息处理领域
中大规模复杂问题求解的有效范式. 从推动大数据挖掘研究角度, 本文首先概要地讨论了大数据 …

The goal of three-way decisions is to divide a universe into three pair-wise disjoint regions
and to act on some or all of these regions using appropriate strategies. The decision …