Fuzzy rough set theory is a powerful tool to deal with uncertainty information, which has
been successfully applied to the fields of attribute reduction, rule extraction, classification …

For heterogeneous data sets containing numerical and symbolic feature values, feature
selection based on fuzzy neighborhood multigranulation rough sets (FNMRS) is a very …

Fuzzy and intuitionistic fuzzy β covering has attracted the interest of many researchers
recently. However, some of the factors namely 1. the lack of inclusion relationship between …

Feature selection facilitates pattern recognition, and fuzzy neighborhood rough sets provide
an effective tool. By fuzzy neighborhood rough sets, we propose a heuristic feature selection …

Removing the redundant features from massive data collected from power systems is of
paramount importance in improving the efficiency of data-driven diagnostic systems. This …

Due to technological advancement, data size has seen a significant increase both in terms
of features and instances. An efficient way to handle large sized datasets is to apply data …

Categorical data is an important class of data in machine learning. Information system based
on categorical data is called a categorical information system (CIS), a CIS with missing …

Feature selection benefits machine learning and knowledge acquisition, and it usually
resorts to various intelligent methodologies. Fuzzy rough sets act as a powerful platform of …

This paper presents an integrated approach, of a multi-criteria decision-making framework
and fuzzy multi-objective programming to optimize dispatching and rebalancing for Ride …

Selection of important attributes/features from decision information systems plays a vital role
in data mining and machine learning tasks. It is regarded as a very interesting, but challenge …