Optimal scale generation in two-class dominance decision tables with sequential three-way decision.

Optimal scale selection is studied widely at present to obtain scale rules in multi-scale decision tables. However, one limitation of this method is that it cannot directly extract scale rules from single-scale decision tables. In this study, we determine how to generate an optimal scale in two-class dominance decision tables with sequential three-way decision (S3WD). First, we use the variance of each attribute to describe its importance in a single-scale two-class dominance decision table. Then, we arrange all attributes in descending order in accordance with their importance. Second, we examine each attribute by following the aforementioned order. Thereafter, we construct different object granules on the basis of the numerical size of attribute value and different decision values and then expand them individually until their limit is reached. Third, we label and delete objects that are already in the object granules. Then, we continue to construct object granules for the remaining objects by following the preceding method until all the objects are labeled (if the information system is inconsistent, then it should be labeled with "until it cannot be marked"). In the process above, we adopt the idea of S3WD and successively classify the objects into positive, boundary, and negative regions in accordance with the three states of objects (i.e., object classification has been confirmed, to be confirmed, and cannot be confirmed). We perform experiments on some UCI datasets and demonstrate the effectiveness of our method. In summary, our work provides a method for generating an optimal scale in single-scale decision tables and extracting scale rules from the generated tables. [ABSTRACT FROM AUTHOR]