Abstract
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.
Highlights
Rough set theory, proposed by Pawlak in 1982 [1], is a powerful mathematical analysis methodology for handling uncertainty, imprecision and fuzziness information
In the dynamic decision systems, techniques of dynamic attribute reduction have attracted the attention of many scholars, whose researches mainly focus on three aspects: variation of the object set, variation of the attribute set and variation of attributes value
For hybrid fuzzy rough set systems, Zeng et al [23] presented the hybrid distance for many types of data, researched updating mechanisms under the variation of the attribute set and proposed incremental reduction algorithms to update reduct
Summary
Rough set theory, proposed by Pawlak in 1982 [1], is a powerful mathematical analysis methodology for handling uncertainty, imprecision and fuzziness information. For hybrid fuzzy rough set systems, Zeng et al [23] presented the hybrid distance for many types of data, researched updating mechanisms under the variation of the attribute set and proposed incremental reduction algorithms to update reduct. For the incomplete decision systems, when the attribute values of single object or multiple objects are changed, Shu et al [28] developed updating mechanisms of positive region, and proposed an incremental reduction algorithm. For the object and attribute sets change simultaneously, Jing et al [32] researched updating strategies of computing reduct and proposed incremental reduction algorithms based on relative knowledge granulation. For three variations of the attribute set, three incremental strategies of updating conflict region for dynamic decision systems are researched, and the acceleration strategies for computing significance measures of candidate attributes and removing redundant attributes based on conflict region are discussed to improve algorithm performance.
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