Sequential 3WD-based local optimal scale selection in dynamic multi-scale decision information systems

Abstract

The multi-scale decision information system (MDIS) is a typical granular computing model. In the research of MDIS, uncertainty is an important factor in making decision analysis, and the selection of optimal scale is a core problem. Therefore, the uncertainty of decision is an important factor in the scale selection. With the rapid increase of data size, the amount of feature information will increase greatly, and the uncertainty of the system will become more and more complex, which makes the optimal scale selection more difficult. The purpose of this study is to investigate the updating law of the local optimal scale under the condition of the dynamic increase of objects. The criterion of scale selection is to keep the uncertainty of the system unchanged. Firstly, the updating law of uncertainty for the decision class in a decision information system under the case of object increment is explored. Secondly, the definition of local optimal scale which keeps the uncertainty of decision classes is given by the sequential three-way decision theory, and the updating law of optimal scale is given by using the updating mechanism of the uncertainty of decision classes. Finally, experiments are conducted to verify the correctness and effectiveness of the proposed method in the calculation of local optimal scale by comparing the algorithms for adding multiple objects directly and adding objects one by one.