Incremental update of rough set approximation under the grade indiscernibility relation

DOI

10.2991/ijcis.2017.10.1.15How to use a DOI?

Keywords

Rough set; Fuzzy relation; The grade indiscernibility relation; Incremental learning; Approximation operators

Abstract

The incremental updating of lower and upper approximations under the variation of information systems is an important issue in rough set theory. Many incremental updating approaches with respect to different kinds of indiscernibility relations have been proposed. The grade indiscernibility relation is a fuzzification of classical Pawlak’s indiscernibility relation which can characterize the similarity between objects more precisely. Based on fuzzy rough set model, this paper discusses the approaches for dynamically acquiring of the upper and lower approximations with respect to the grade indiscernibility relation when adding and removing an attribute or an object, and changing the attribute value of the object, respectively. Since the approaches are used in succession, they make the approximations can be updated correctly and effectively when any kind of possible change in the information system. Finally, extensive experiments on data sets from University of California, Irvine (UCI) show that the incremental methods effectively reduce the computing time in comparison with the traditional non-incremental method.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Junfang Luo
AU  - Yaya Liu
AU  - Keyun Qin
AU  - Heng Ding
PY  - 2017
DA  - 2017/01/01
TI  - Incremental update of rough set approximation under the grade indiscernibility relation
JO  - International Journal of Computational Intelligence Systems
SP  - 212
EP  - 233
VL  - 10
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2017.10.1.15
DO  - 10.2991/ijcis.2017.10.1.15
ID  - Luo2017
ER  -