Purpose–The purpose of this paper is to introduce a methodology to identify sustainable
supply chain key performance indicators (KPI) that can then be used for sustainability …

Feature selection is an important preprocessing step in machine learning and data mining.
In real-world applications, costs, including money, time and other resources, are required to …

In this paper, we propose an efficient quick attribute reduct algorithm based on
neighborhood rough set model. In this algorithm we divide the objects (records) of the whole …

As a soft computing tool, rough set theory has become a popular mathematical framework
for pattern recognition, data mining and knowledge discovery. It can only deal with attributes …

Attribute reduction is the key technique for knowledge acquisition in rough set theory.
However, it is still a challenging task to perform attribute reduction on massive data. During …

Feature selection tries to find a subset of feature from a larger feature pool and the selected
subset can provide the same or even better performance compared with using the whole set …

As the volume of data grows at an unprecedented rate, large-scale data mining and
knowledge discovery present a tremendous challenge. Rough set theory, which has been …

Attribute reduction has become an important step in pattern recognition and machine
learning tasks. Covering rough sets, as a generalization of classical rough sets, have …

Nowadays, with the volume of data growing at an unprecedented rate, large-scale data
mining and knowledge discovery have become a new challenge. Rough set theory for …

Feature selection is an important data preprocessing technique, and has been widely
studied in data mining, machine learning, and granular computing. However, very little …