The data processing under a multi-scale framework can satisfy the problem analysis from different perspectives, and the multi-scale rough set model promoted the development of multi-scale data analysis. The optimal cut in multi-scale decision systems can enable different objects to take different scales under the same attribute, and thus realize knowledge acquisition across granularity levels in multi-scale information systems. However, in practical applications under the network environment, the data in the information system often changes dynamically. In order to solve the dynamic updating of the optimal cut in multi-scale decision systems, the stepwise updating method of optimal cut while attributes dynamic changing in multi-scale decision systems are proposed based on the static stepwise optimal cut selection algorithm. Firstly, an initial cut combination and attribute sequence are determined from the original systems with unchanged information, which avoids the recalculating of attribute sequence when updating the optimal cut dynamically. Secondly, two simplified theorems for node consistence judgement are proposed, which can shorten the time of determining node consistence. Finally, a stepwise updating algorithm of optimal cut is proposed considering the dynamic changes of attributes. Multiple comparative experiments on the UCI standard dataset show that, compared with the static stepwise optimal cut selection method, the proposed dynamic updating algorithm can correctly obtain an optimal cut while significantly improving the computational efficiency.

Guangyao Dai, Yin Wang, Ting Gong. Stepwise updating of optimal cut in multi-scale decision systems considering the dynamic changes of attributes. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 5: 1-8. https://doi.org/10.25236/AJCIS.2023.060501.

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