Premium
Dynamic Maintenance of Approximations in Dominance‐Based Rough Set Approach under the Variation of the Object Set
Author(s) -
Li Shaoyong,
Li Tianrui,
Liu Dun
Publication year - 2013
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21599
Subject(s) - rough set , computer science , dominance based rough set approach , data mining , object (grammar) , set (abstract data type) , artificial intelligence , machine learning , algorithm , mathematics , programming language
Dominance‐based rough sets approach (DRSA) is an effective tool to deal with information with preference‐ordered attribute domains and decision classes. Any information system may evolve when new objects enter into or old objects get out. Approximations of DRSA need update for decision analysis or other relative tasks. Incremental updating is a feasible and effective technique to update approximations. The purpose of this paper is to present an incremental approach for updating approximations of DRSA. The approach is applicable to dynamic information systems when the set of objects varies over time. In this paper, we discuss the principles of incrementally updating P ‐dominating sets and P ‐dominated sets and propose an incremental approach for updating approximations of DRSA. A numerical example is given to illustrate the incremental approach. The experimental evaluations on data sets from UCI show that the incremental approach outperforms the original nonincremental one.