Decision-Theoretic Rough Set: A Fusion Strategy

Decision-theoretic rough set is a popular topic. However, such single-granulation rough set model is not able to handle complex information well, such as multi-source, multi-scale and high dimensions data. Therefore, the fusion of the ideas of Bayesian decision and multi-granulation may be an appeal...

Full description

Bibliographic Details
Main Authors: Tao Yin, Xiaojuan Mao, Ying Zhang, Yiting Ma, Hengrong Ju, Weiping Ding
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9284436/
_version_ 1811274223581134848
author Tao Yin
Xiaojuan Mao
Ying Zhang
Yiting Ma
Hengrong Ju
Weiping Ding
author_facet Tao Yin
Xiaojuan Mao
Ying Zhang
Yiting Ma
Hengrong Ju
Weiping Ding
author_sort Tao Yin
collection DOAJ
description Decision-theoretic rough set is a popular topic. However, such single-granulation rough set model is not able to handle complex information well, such as multi-source, multi-scale and high dimensions data. Therefore, the fusion of the ideas of Bayesian decision and multi-granulation may be an appealing issue. In this article, a novel rough set model based on multi-granularity decision theory is proposed. The discussed rough set model not only overcomes the shortcomings of optimistic and pessimistic rough sets, but also gains high approximation quality and low decision cost at the same time with a satisfactory threshold. In information granule reduction, heuristic and genetic algorithms are used to compute reducts based on three different criteria, respectively. The experimental results express that decision preservation based reduction may not suitable in such rough set models. Moreover, we also reveal that decision monotony and cost minimum based reductions are able to be popular research topics in rough set model of multi-granulation decision theory.
first_indexed 2024-04-12T23:15:06Z
format Article
id doaj.art-92c8bf096bd2495b975a00441b66e910
institution Directory Open Access Journal
issn 2169-3536
language English
last_indexed 2024-04-12T23:15:06Z
publishDate 2020-01-01
publisher IEEE
record_format Article
series IEEE Access
spelling doaj.art-92c8bf096bd2495b975a00441b66e9102022-12-22T03:12:43ZengIEEEIEEE Access2169-35362020-01-01822102722103810.1109/ACCESS.2020.30427999284436Decision-Theoretic Rough Set: A Fusion StrategyTao Yin0Xiaojuan Mao1Ying Zhang2Yiting Ma3Hengrong Ju4https://orcid.org/0000-0001-9894-9844Weiping Ding5https://orcid.org/0000-0002-3180-7347School of Information Science and Technology, Nantong University, Nantong, ChinaDepartment of Respiratory Medicine, The Sixth People’s Hospital of Nantong/Affiliated Nantong Hospital, Shanghai University, Nantong, ChinaSchool of Information Science and Technology, Nantong University, Nantong, ChinaSchool of Information Science and Technology, Nantong University, Nantong, ChinaSchool of Information Science and Technology, Nantong University, Nantong, ChinaSchool of Information Science and Technology, Nantong University, Nantong, ChinaDecision-theoretic rough set is a popular topic. However, such single-granulation rough set model is not able to handle complex information well, such as multi-source, multi-scale and high dimensions data. Therefore, the fusion of the ideas of Bayesian decision and multi-granulation may be an appealing issue. In this article, a novel rough set model based on multi-granularity decision theory is proposed. The discussed rough set model not only overcomes the shortcomings of optimistic and pessimistic rough sets, but also gains high approximation quality and low decision cost at the same time with a satisfactory threshold. In information granule reduction, heuristic and genetic algorithms are used to compute reducts based on three different criteria, respectively. The experimental results express that decision preservation based reduction may not suitable in such rough set models. Moreover, we also reveal that decision monotony and cost minimum based reductions are able to be popular research topics in rough set model of multi-granulation decision theory.https://ieeexplore.ieee.org/document/9284436/Decision costinformation granule reductionmultigranulationoptimizationrough set
spellingShingle Tao Yin
Xiaojuan Mao
Ying Zhang
Yiting Ma
Hengrong Ju
Weiping Ding
Decision-Theoretic Rough Set: A Fusion Strategy
IEEE Access
Decision cost
information granule reduction
multigranulation
optimization
rough set
title Decision-Theoretic Rough Set: A Fusion Strategy
title_full Decision-Theoretic Rough Set: A Fusion Strategy
title_fullStr Decision-Theoretic Rough Set: A Fusion Strategy
title_full_unstemmed Decision-Theoretic Rough Set: A Fusion Strategy
title_short Decision-Theoretic Rough Set: A Fusion Strategy
title_sort decision theoretic rough set a fusion strategy
topic Decision cost
information granule reduction
multigranulation
optimization
rough set
url https://ieeexplore.ieee.org/document/9284436/
work_keys_str_mv AT taoyin decisiontheoreticroughsetafusionstrategy
AT xiaojuanmao decisiontheoreticroughsetafusionstrategy
AT yingzhang decisiontheoreticroughsetafusionstrategy
AT yitingma decisiontheoreticroughsetafusionstrategy
AT hengrongju decisiontheoreticroughsetafusionstrategy
AT weipingding decisiontheoreticroughsetafusionstrategy