Presently, the mechanism of multi-granularity has been frequently realized by various
mathematical tools in Granular Computing especially rough set. Nevertheless, as a key topic …

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 …

Crude oil price forecasting has attracted much attention due to its significance on
commodities market as well as nonlinear complexity in prediction task. Combining forecasts …

Heat load prediction is essential to discover blast furnace (BF) anomalies in time and take
measures in advance to reduce erosion in the ironmaking process. However, owing to the …

As a prominent model of granular computing, neighborhood rough set provides clear
granularity organization and expression in terms of inherent parameter (neighborhood …

In rough set theory, the concept of conditional entropy has been widely accepted for
studying the problem of attribute reduction. If a searching strategy is given to find reduct …

Attribute reduction, also known as feature selection, is a common problem by selecting a
subset of relevant attributes (eg features) to reach efficient learning/mining. Many attribute …

Hierarchical feature selection addresses the issues caused by the presence of high-
dimensional features in multi-category classification systems with hierarchical structures …

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 …

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 …