Efficient knowledge updating method for inconsistent decision tables

With the continuous expansion of data from different domains, there are lots of inconsistent data in decision tables. Approximations are fundamental notions of rough set theory. However, inconsistent decision tables (IDTs) may update with new coming objects due to the information collection and update. In this circumstance, how to dynamically acquire useful approximations with previous knowledge in IDTs is crucial for data analysis and knowledge discovery. To address this issue, we focus on exploiting incremental approaches for computing approximations when objects are changing in IDTs over time, which is of great significance for gaining decision rules in the context of dynamic environments. First, we introduce the concepts of inconsistent decision tables and approximations. Furthermore, with dynamic changing objects, several principles of updating approximations are investigated according to the positive and boundary regions. Following the proposed principles, incremental algorithms for acquiring approximations in IDTs are presented while the objects change with time. In the end, comparative results are illustrated to verify effectiveness of updating lower and upper approximations in IDTs, displaying the advantage of the proposed approaches.