Incremental Attribute Reduction Under Variations of the Attribute Set Based on Conflict Region

In real application, the attribute set of decision systems may vary with time. How to efficiently update the reduct becomes one of important tasks in the knowledge discovery. The static methods for updating knowledge need to recalculate when the attribute set varies every time, which makes it potentially very time-consuming to update knowledge, especially if the data sets are growled rapidly. Incremental learning method is an efficient technique for knowledge discovery in the dynamic system. In the dynamic system of the attribute set variations, there exist three kinds of attribute set changes: addition of the attribute set, deletion of the attributes and simultaneous variation of adding and deleting the attribute sets. The objective of this paper is to study the incremental reduction algorithm based on conflict region in the dynamic decision system. For three variations of the attribute set, we firstly introduce incremental mechanisms of updating conflict region; and then we research acceleration strategies for calculating significance measures of candidate attributes and eliminating redundant attributes. Consequently, a unified incremental reduction algorithm based on conflict region is presented for three variations of the attribute set. Finally, we design a series of experiments which are performed on 12 UCI data sets. The experimental results indicate that the proposed incremental methods can effectively to update the reduct with variations of the attribute set, and its efficiency is much better than that of static algorithms.