Abstract
Approximation computation is a critical step in rough sets theory used in knowledge discovery and other related tasks. In practical applications, an information system often evolves over time by the variation of attributes or objects. Effectively computing approximations is vital in data mining. Dominance-based rough set approach can handle information with preference-ordered attribute domain, but it is not able to handle the situation of data missing. Confidential Dominance-based Rough Set Approach (CDRSA) is introduced to process Incomplete Ordered Decision System (IODS). This paper focuses on incremental updating approximations under dynamic environment in IODS. With the CDRSA, the principles of incremental updating approximations are discussed while the variation of attribute sets or the union of subsets of objects and the corresponding incremental algorithms are developed. Comparative experiments on data sets of UCI and results show that the proposed incremental approaches can improve the performance of updating approximations effectively by a significant shortening of the computational time.
Highlights
Rough set theory is proposed by Pawlak[1] to deal with inconsistency problems, which is useful in fields such as knowledge discovery[2,3,4], decision analysis[5,6], data mining[7,8], etc
Chen et al proposed an incremental method for updating approximations while objects dynamically alter and attributes values vary in variable precision rough set model[49]
Incremental updating approximations of Confidential Dominance-based Rough Set Approach (CDRSA) is discussed when subsets of objects are merged into Incomplete Ordered Decision System (IODS)
Summary
Rough set theory is proposed by Pawlak[1] to deal with inconsistency problems, which is useful in fields such as knowledge discovery[2,3,4], decision analysis[5,6], data mining[7,8], etc. Błaszczynski et al discussed different ways of handling missing values in sorting problems with monotonicity constraints in DRSA19 He et al investigated an extend dominance relation to discover knowledge from approximations[20]. Li et al proposed an incremental approach for updating approximations under rough set based the characteristic relation when adding or removing some attributes in incomplete information system[31]. Liu et al presented approaches for incremental updating approximations in probabilistic rough sets under the variation of attributes[35]. Chen et al proposed an incremental method for updating approximations while objects dynamically alter and attributes values vary in variable precision rough set model[49]. This paper focuses on incremental methods for updating approximations of confidential dominancebased rough set under the variation of attributes or objects in IODS.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
