Multi-label learning deals with data belonging to different labels simultaneously. Like
traditional supervised feature selection, multi-label feature selection also plays an important …

Fuzzy rough set method provides an effective approach to data mining and knowledge
discovery from hybrid data including categorical values and numerical values. However, its …

Attribute reduction is one of the important research issues in rough set theory. Most existing
attribute reduction algorithms are now faced with two challenging problems. On one hand …

Covering rough set theory provides an effective approach to dealing with uncertainty in data
analysis. Knowledge acquisition is a main issue in covering rough set theory. However, the …

There has been a growing interest in describing the difficulty of solving a classification
problem. This knowledge can be used, among other things, to support more grounded …

Symbolic value partitioning is a knowledge reduction technique in the field of data mining. In
this paper, we propose a granular computing approach for the partitioning task that includes …

Abstract Evaluation functions, used to measure the quality of features, have great influence
on the feature selection algorithms in areas of data mining and knowledge discovery …

These days, the volume of information is developing at an uncommon rate, enormous
information mining, and learning revelation have turned into another test in the time of …