Feature selection aims to select a feature subset from an original feature set based on a
certain evaluation criterion. Since feature selection can achieve efficient feature reduction, it …

Dominance-based rough approximation discovers inconsistencies from ordered criteria and
satisfies the requirement of the dominance principle between single-valued domains of …

Incomplete decision contexts are a kind of decision formal contexts in which information
about the relationship between some objects and attributes is not available or is lost …

Granular computing and knowledge reduction are two basic issues in knowledge
representation and data mining. Granular structure of concept lattices with application in …

This chapter emphasizes on the role played by rough set theory (RST) within the broad field
of Machine Learning (ML). As a sound data analysis and knowledge discovery paradigm …

Kernel methods and rough sets are two general pursuits in the domain of machine learning
and intelligent systems. Kernel methods map data into a higher dimensional feature space …

Incremental learning strategy based feature selection approaches can improve the efficiency
of reduction algorithm used for datasets with dynamic characteristic, which has attracted …

As a soft computing tool, rough set theory has become a popular mathematical framework
for pattern recognition, data mining and knowledge discovery. It can only deal with attributes …

Interval-valued data describe the random phenomenon that abounds in the real world, a
pivotal research orientation in uncertainty processing. With the rapid development of big …

Feature selection has become one of the hot research topics in the era of big data. At the
same time, as an extension of single-valued data, interval-valued data with its inherent …