Incremental update of approximations in dominance-based rough sets approach under the variation of attribute values

Dominance-based Rough Sets Approach (DRSA) has received much attention since it is able to acquire knowledge from information with preference ordered attribute domains and decision classes. In many real-life applications, the information systems may evolve over time dynamically. In a dynamic information system, the obtained knowledge, e.g., approximations in DRSA, need to be updated for decision making and other related tasks. As a useful technique, the incremental update can be applied to process dynamic information with revising the obtained knowledge. In this paper, we propose an incremental approach for maintaining approximations of DRSA when attribute values vary over time. Some numerical examples illustrate that the incremental approach can renew approximations of DRSA without beginning from scratch. Experimental evaluations show that the incremental algorithm can effectively reduce the computational time in comparison with the non-incremental one when the ratio of the attribute values varied is less than a threshold.