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...
Main Authors: | , , , , , |
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Format: | Article |
Language: | English |
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IEEE
2020-01-01
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Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9284436/ |
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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/ |
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