Optimal scale selection and knowledge discovery in generalized multi-scale decision tables

Abstract

In the real world, each attribute of data sets has multiple scales, whether it is a conditional attribute or a decision attribute. Knowledge mined from data based on the optimal scale can well meet the needs of real life. Hence, there is no denying that optimal scale selection is an problem to be solved urgently in the knowledge discovery field of multi-scale decision tables. The optimal scale combines coarser condition attributes with the finer decision attribute, so as to achieve a balance between efficiency and accuracy. With the aim of selecting the optimal scale combination, we firstly explore some related properties and theorems of generalized multi-scale decision tables. Then we define the optimal scale in generalized multi-scale decision tables and propose two algorithms for optimal scale selection. In addition, a knowledge acquisition algorithm and a multi-scale rough set classifier framework are proposed. Finally, numerical experiments are performed on some open data sets to test the effectiveness of the algorithms.