Dynamic composite decision-theoretic rough set under the change of attributes

DOI

10.2991/ijcis.11.1.27How to use a DOI?

Keywords

Composite information table; Decision-theoretic rough set; Quantitative composite relation; Matrix; Incremental updating

Abstract

In practical decision-making, we prefer to characterize the uncertain problems with the hybrid data, which consists of various types of data, e.g., categorical data, numerical dada, interval-valued data and set-valued data. The extended rough sets can deal with single type of data based on specific binary relation, including the equivalence relation, neighborhood relation, partial order relation, tolerance relation, etc. However, the fusion of these relations is a significant challenge task in such composite information table. To tackle this issue, this paper proposes the intersection and union composite relation, and further introduces a quantitative composite decision-theoretic rough set model. Subsequently, we present a novel matrix-based approach to compute the upper and lower approximations in proposed model. Moreover, we propose the incremental updating mechanisms and algorithms under the addition and deletion of attributes. Finally, experimental valuations are conducted to illustrate the efficiency of proposed method and algorithms.

Copyright

© 2018, 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  - Linna Wang
AU  - Xin Yang
AU  - Yong Chen
AU  - Ling Liu
AU  - Shiyong An
AU  - Pan Zhuo
PY  - 2018
DA  - 2018/01/01
TI  - Dynamic composite decision-theoretic rough set under the change of attributes
JO  - International Journal of Computational Intelligence Systems
SP  - 355
EP  - 370
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.27
DO  - 10.2991/ijcis.11.1.27
ID  - Wang2018
ER  -