Incremental Versus Non-incremental: Data and Algorithms Based on Ordering Relations - Xiuyi Jia; Lin Shang; Jiajun Chen; Xinyu Dai (International Journal of Computational Intelligence Systems, 2011)
Based on multi-dominance discernibility matrices, a non-incremental algorithm RIDDM and an incremental algorithm INRIDDM are proposed by means of Dominance-based Rough Set Approach. For the incremental algorithm, when a new object arrives, after updating one row or one column in the matrix, we could get the updated rule sets. Time complexity analysis and experimental results show that the incremental algorithm INRIDDM is superior to some other non-incremental algorithms when dealing with large data sets. This paper also explores the influence of data saturation and data concentration on rule induction algorithms. We come to conclude that the data saturation and data concentration are important for the performance analysis of one learning algorithm.