Parameter Reductions of Bipolar Fuzzy Soft Sets with Their Decision-Making Algorithms

Parameter reduction is a very important technique in many fields, including pattern recognition. Many reduction techniques have been reported for fuzzy soft sets to solve decision-making problems. However, there is almost no attention to the parameter reduction of bipolar fuzzy soft sets, which take...
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Autor*in:	Ghous Ali [verfasserIn] Muhammad Akram [verfasserIn] Ali N. A. Koam [verfasserIn] José Carlos R. Alcantud [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	bipolar fuzzy soft set soft set parameter reduction algorithm decision-making

Übergeordnetes Werk:	In: Symmetry - MDPI AG, 2009, 11(2019), 8, p 949
Übergeordnetes Werk:	volume:11 ; year:2019 ; number:8, p 949

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/sym11080949

Katalog-ID:	DOAJ031158420

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spelling	10.3390/sym11080949 doi (DE-627)DOAJ031158420 (DE-599)DOAJ2cb0632787a24a05aff0091288989272 DE-627 ger DE-627 rakwb eng QA1-939 Ghous Ali verfasserin aut Parameter Reductions of Bipolar Fuzzy Soft Sets with Their Decision-Making Algorithms 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Parameter reduction is a very important technique in many fields, including pattern recognition. Many reduction techniques have been reported for fuzzy soft sets to solve decision-making problems. However, there is almost no attention to the parameter reduction of bipolar fuzzy soft sets, which take advantage of the fact that membership and non-membership degrees play a symmetric role. This methodology is of great importance in many decision-making situations. In this paper, we provide a novel theoretical approach to solve decision-making problems based on bipolar fuzzy soft sets and study four types of parameter reductions of such sets. Parameter reduction algorithms are developed and illustrated through examples. The experimental results prove that our proposed parameter reduction techniques delete the irrelevant parameters while keeping definite decision-making choices unchanged. Moreover, the reduction algorithms are compared regarding the degree of ease of computing reduction, applicability, exact degree of reduction, applied situation, and multi-use of parameter reduction. Finally, a real application is developed to describe the validity of our proposed reduction algorithms. bipolar fuzzy soft set soft set parameter reduction algorithm decision-making Mathematics Muhammad Akram verfasserin aut Ali N. A. Koam verfasserin aut José Carlos R. Alcantud verfasserin aut In Symmetry MDPI AG, 2009 11(2019), 8, p 949 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:11 year:2019 number:8, p 949 https://doi.org/10.3390/sym11080949 kostenfrei https://doaj.org/article/2cb0632787a24a05aff0091288989272 kostenfrei https://www.mdpi.com/2073-8994/11/8/949 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2019 8, p 949
allfields_unstemmed	10.3390/sym11080949 doi (DE-627)DOAJ031158420 (DE-599)DOAJ2cb0632787a24a05aff0091288989272 DE-627 ger DE-627 rakwb eng QA1-939 Ghous Ali verfasserin aut Parameter Reductions of Bipolar Fuzzy Soft Sets with Their Decision-Making Algorithms 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Parameter reduction is a very important technique in many fields, including pattern recognition. Many reduction techniques have been reported for fuzzy soft sets to solve decision-making problems. However, there is almost no attention to the parameter reduction of bipolar fuzzy soft sets, which take advantage of the fact that membership and non-membership degrees play a symmetric role. This methodology is of great importance in many decision-making situations. In this paper, we provide a novel theoretical approach to solve decision-making problems based on bipolar fuzzy soft sets and study four types of parameter reductions of such sets. Parameter reduction algorithms are developed and illustrated through examples. The experimental results prove that our proposed parameter reduction techniques delete the irrelevant parameters while keeping definite decision-making choices unchanged. Moreover, the reduction algorithms are compared regarding the degree of ease of computing reduction, applicability, exact degree of reduction, applied situation, and multi-use of parameter reduction. Finally, a real application is developed to describe the validity of our proposed reduction algorithms. bipolar fuzzy soft set soft set parameter reduction algorithm decision-making Mathematics Muhammad Akram verfasserin aut Ali N. A. Koam verfasserin aut José Carlos R. Alcantud verfasserin aut In Symmetry MDPI AG, 2009 11(2019), 8, p 949 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:11 year:2019 number:8, p 949 https://doi.org/10.3390/sym11080949 kostenfrei https://doaj.org/article/2cb0632787a24a05aff0091288989272 kostenfrei https://www.mdpi.com/2073-8994/11/8/949 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2019 8, p 949
allfieldsGer	10.3390/sym11080949 doi (DE-627)DOAJ031158420 (DE-599)DOAJ2cb0632787a24a05aff0091288989272 DE-627 ger DE-627 rakwb eng QA1-939 Ghous Ali verfasserin aut Parameter Reductions of Bipolar Fuzzy Soft Sets with Their Decision-Making Algorithms 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Parameter reduction is a very important technique in many fields, including pattern recognition. Many reduction techniques have been reported for fuzzy soft sets to solve decision-making problems. However, there is almost no attention to the parameter reduction of bipolar fuzzy soft sets, which take advantage of the fact that membership and non-membership degrees play a symmetric role. This methodology is of great importance in many decision-making situations. In this paper, we provide a novel theoretical approach to solve decision-making problems based on bipolar fuzzy soft sets and study four types of parameter reductions of such sets. Parameter reduction algorithms are developed and illustrated through examples. The experimental results prove that our proposed parameter reduction techniques delete the irrelevant parameters while keeping definite decision-making choices unchanged. Moreover, the reduction algorithms are compared regarding the degree of ease of computing reduction, applicability, exact degree of reduction, applied situation, and multi-use of parameter reduction. Finally, a real application is developed to describe the validity of our proposed reduction algorithms. bipolar fuzzy soft set soft set parameter reduction algorithm decision-making Mathematics Muhammad Akram verfasserin aut Ali N. A. Koam verfasserin aut José Carlos R. Alcantud verfasserin aut In Symmetry MDPI AG, 2009 11(2019), 8, p 949 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:11 year:2019 number:8, p 949 https://doi.org/10.3390/sym11080949 kostenfrei https://doaj.org/article/2cb0632787a24a05aff0091288989272 kostenfrei https://www.mdpi.com/2073-8994/11/8/949 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2019 8, p 949
allfieldsSound	10.3390/sym11080949 doi (DE-627)DOAJ031158420 (DE-599)DOAJ2cb0632787a24a05aff0091288989272 DE-627 ger DE-627 rakwb eng QA1-939 Ghous Ali verfasserin aut Parameter Reductions of Bipolar Fuzzy Soft Sets with Their Decision-Making Algorithms 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Parameter reduction is a very important technique in many fields, including pattern recognition. Many reduction techniques have been reported for fuzzy soft sets to solve decision-making problems. However, there is almost no attention to the parameter reduction of bipolar fuzzy soft sets, which take advantage of the fact that membership and non-membership degrees play a symmetric role. This methodology is of great importance in many decision-making situations. In this paper, we provide a novel theoretical approach to solve decision-making problems based on bipolar fuzzy soft sets and study four types of parameter reductions of such sets. Parameter reduction algorithms are developed and illustrated through examples. The experimental results prove that our proposed parameter reduction techniques delete the irrelevant parameters while keeping definite decision-making choices unchanged. Moreover, the reduction algorithms are compared regarding the degree of ease of computing reduction, applicability, exact degree of reduction, applied situation, and multi-use of parameter reduction. Finally, a real application is developed to describe the validity of our proposed reduction algorithms. bipolar fuzzy soft set soft set parameter reduction algorithm decision-making Mathematics Muhammad Akram verfasserin aut Ali N. A. Koam verfasserin aut José Carlos R. Alcantud verfasserin aut In Symmetry MDPI AG, 2009 11(2019), 8, p 949 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:11 year:2019 number:8, p 949 https://doi.org/10.3390/sym11080949 kostenfrei https://doaj.org/article/2cb0632787a24a05aff0091288989272 kostenfrei https://www.mdpi.com/2073-8994/11/8/949 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2019 8, p 949
language	English
source	In Symmetry 11(2019), 8, p 949 volume:11 year:2019 number:8, p 949
sourceStr	In Symmetry 11(2019), 8, p 949 volume:11 year:2019 number:8, p 949
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institution	findex.gbv.de
topic_facet	bipolar fuzzy soft set soft set parameter reduction algorithm decision-making Mathematics
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authorswithroles_txt_mv	Ghous Ali @@aut@@ Muhammad Akram @@aut@@ Ali N. A. Koam @@aut@@ José Carlos R. Alcantud @@aut@@
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author	Ghous Ali
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topic_title	QA1-939 Parameter Reductions of Bipolar Fuzzy Soft Sets with Their Decision-Making Algorithms bipolar fuzzy soft set soft set parameter reduction algorithm decision-making
topic	misc QA1-939 misc bipolar fuzzy soft set misc soft set misc parameter reduction misc algorithm misc decision-making misc Mathematics
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title	Parameter Reductions of Bipolar Fuzzy Soft Sets with Their Decision-Making Algorithms
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title_sort	parameter reductions of bipolar fuzzy soft sets with their decision-making algorithms
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title_auth	Parameter Reductions of Bipolar Fuzzy Soft Sets with Their Decision-Making Algorithms
abstract	Parameter reduction is a very important technique in many fields, including pattern recognition. Many reduction techniques have been reported for fuzzy soft sets to solve decision-making problems. However, there is almost no attention to the parameter reduction of bipolar fuzzy soft sets, which take advantage of the fact that membership and non-membership degrees play a symmetric role. This methodology is of great importance in many decision-making situations. In this paper, we provide a novel theoretical approach to solve decision-making problems based on bipolar fuzzy soft sets and study four types of parameter reductions of such sets. Parameter reduction algorithms are developed and illustrated through examples. The experimental results prove that our proposed parameter reduction techniques delete the irrelevant parameters while keeping definite decision-making choices unchanged. Moreover, the reduction algorithms are compared regarding the degree of ease of computing reduction, applicability, exact degree of reduction, applied situation, and multi-use of parameter reduction. Finally, a real application is developed to describe the validity of our proposed reduction algorithms.
abstractGer	Parameter reduction is a very important technique in many fields, including pattern recognition. Many reduction techniques have been reported for fuzzy soft sets to solve decision-making problems. However, there is almost no attention to the parameter reduction of bipolar fuzzy soft sets, which take advantage of the fact that membership and non-membership degrees play a symmetric role. This methodology is of great importance in many decision-making situations. In this paper, we provide a novel theoretical approach to solve decision-making problems based on bipolar fuzzy soft sets and study four types of parameter reductions of such sets. Parameter reduction algorithms are developed and illustrated through examples. The experimental results prove that our proposed parameter reduction techniques delete the irrelevant parameters while keeping definite decision-making choices unchanged. Moreover, the reduction algorithms are compared regarding the degree of ease of computing reduction, applicability, exact degree of reduction, applied situation, and multi-use of parameter reduction. Finally, a real application is developed to describe the validity of our proposed reduction algorithms.
abstract_unstemmed	Parameter reduction is a very important technique in many fields, including pattern recognition. Many reduction techniques have been reported for fuzzy soft sets to solve decision-making problems. However, there is almost no attention to the parameter reduction of bipolar fuzzy soft sets, which take advantage of the fact that membership and non-membership degrees play a symmetric role. This methodology is of great importance in many decision-making situations. In this paper, we provide a novel theoretical approach to solve decision-making problems based on bipolar fuzzy soft sets and study four types of parameter reductions of such sets. Parameter reduction algorithms are developed and illustrated through examples. The experimental results prove that our proposed parameter reduction techniques delete the irrelevant parameters while keeping definite decision-making choices unchanged. Moreover, the reduction algorithms are compared regarding the degree of ease of computing reduction, applicability, exact degree of reduction, applied situation, and multi-use of parameter reduction. Finally, a real application is developed to describe the validity of our proposed reduction algorithms.
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Parameter Reductions of Bipolar Fuzzy Soft Sets with Their Decision-Making Algorithms

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