Attribute reductions are essential pre-processing steps in such as data mining, machine
learning, pattern recognition and many other fields. Moreover, test-cost-sensitive attribute …

Information granules are fundamental, abstract, and easy-to-operate constructs supporting
the human-centered handling way in granular computing (GrC). One of the basic properties …

In order to overcome the limitation of low efficiency of existing granularity reduction
algorithms in multi-granulation rough sets, based on matrix method, a fast granularity …

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 …

As a granular computing model, multi-scale data analysis has attracted considerable
attention in last several years. However, most of multi-scale models are hardly to deal with …

Few-shot learning, which aims to identify new classes with few samples, is an increasingly
popular and crucial research topic in the machine learning. Recently, the development of …

Attribute reduction is a core issue in rough set theory. In recent years, with the fast
development of data processing tools, information systems may increase quickly in objects …

Recently several novel cost‐sensitive attribute‐scale selection approaches have been
proposed based on measurement errors. They are significant because they can …

Forecast combination, a well-established technique for improving forecasting accuracy,
investigates the integration of competing forecasts to produce a composite superior to …

Few-shot learning has become an important branch of machine learning, which aims to give
correct prediction information to unknown samples. Many few-shot models mostly adopt a …