Efficient updating of probabilistic approximations with incremental objects

摘要

Probabilistic rough set model, which is established by incorporating the theory of probability into rough set theory, aims to model imprecise data with the tolerance of decision errors in terms of conditional probability and probabilistic parameters. The volume of data is frequently varied dynamically. It is very time consuming to analyze the updates of data incessantly from computation perspective. Incremental learning technique is desired to improve computational efficiency, which poses an incremental capability for adaptive knowledge maintenance to accommodate varied data. In this paper, we focus on efficient updating of probabilistic approximations with incremental objects in a dynamic information table. The dynamic characteristics of conditional partition and decision classification on the universe are analyzed when the insertion or deletion of objects occurs. Different updating patterns of conditional probability are presented for different combinatorial varieties of the conditional and decision classes. Meanwhile, incremental algorithms for updating probabilistic approximations are proposed, which are proficient to efficiently classify the incremental objects into decision regions by avoiding re-computation efforts. Experiments on benchmark data sets indicate that the proposed algorithms outperform the static algorithm in the presence of dynamic variation of the universe.