The rough topology for numerical data

Abstract

In this paper, we generalize the rough topology and the core to numerical data by classifying objects in terms of the attribute values. A new approach to finding the core for numerical data is discussed. A measurement criterion is introduced to determine whether an attribute belongs to the core. This new method for finding the core is used for attribute reduction. It is tested and compared by using eight different machine-learning algorithms. Also, it is discussed how this material is used to rank the importance of attributes in data classification. Finally, the algorithms and codes for data conversion and core determination are provided.