The scale of the radius for constructing neighborhood relation has a great effect on the
results of neighborhood rough sets and corresponding measures. A very small radius …

In the field of rough set, how to improve the efficiency of obtaining reduct has been paid
much attention to. One of the typical strategies is to reduce the number of comparisons …

Autoencoder network is an efficient representation learning method. In general, a finer
feature set obtained from autoencoder leads to a lower error rate and lower total …

Through abstracting commonness from the existing heuristic algorithms, control strategies
bring us higher level understandings of building reducts in rough set theory. To further …

As one of the crucial topics in the development of rough set, attribute reduction has received
extensive attentions because it is practical and interpretable for us to perform dimensional …

In this study, a new technique of rough-fuzzy clustering based on multigranulation
approximation regions is developed to tackle the uncertainty associated with the fuzzifier …

By considering the information granulation in Granular Computing, the concept of the multi-
granularity is important. It is mainly because different results of information granulation will …

As a fundamental material of Granular Computing, information granulation sheds new light
on the topic of feature selection. Although information granulation has been effectively …

Most existing results about attribute reduction are reported by considering one and only one
granularity, especially for the strategies of searching reducts. Nevertheless, how to derive …

In the era of open machine learning, a kind of data is accompanied by a hierarchical
structure between classes in the label space and the increasing number of features …