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A new consensus mining approach to group ranking problems involving different intensities of preferences.

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Title: A new consensus mining approach to group ranking problems involving different intensities of preferences.
Authors: Ma, Li-Ching1 lcma@nuu.edu.tw
Source: Computers & Industrial Engineering. May2019, Vol. 131, p320-326. 7p.
Subjects: Data mining, Problem solving, Group decision making, Mathematical optimization, Computer users
Abstract: • A total ranking list involving group consensus preferences is achieved. • A group consensus mining approach is proposed without candidate generation. • An optimization model involving maximum consensus sequences is developed. • Flexibility is provided in solving ranking problems using different input formats. • Minimum consensus levels and maximum disagreement levels are adjustable. Discovering the group priority from a set of user preferences plays an important role in group decision making because of its extensive applications in practice. In most group ranking problems, users are supposed to input a ranking of alternatives regardless of the intensities of preference. However, if two users specify that they prefer A to B, the intensities of preference may be quite different. In addition, most researchers have tried to determine a total ranking list by minimizing total differences among user preferences, but users might have little consensus on the final results. This study aims to propose a new consensus-based approach for group ranking problems involving different intensities of preference. Stemming from the concept of consensus mining, consensus relationships are discovered by three accumulation matrices and consensus thresholds. An optimization model incorporating the consensus relationships and the concept of Borda majority count is then developed to derive a total ranking list. Compared to previous studies, the proposed approach can treat group ranking problems involving different intensities of preference, reduce the occurrence of ties, and achieve a total ranking list reflecting the consensus preference of the majority of users. [ABSTRACT FROM AUTHOR]
Copyright of Computers & Industrial Engineering is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 136088639
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: A new consensus mining approach to group ranking problems involving different intensities of preferences.
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  Data: <searchLink fieldCode="AR" term="%22Ma%2C+Li-Ching%22">Ma, Li-Ching</searchLink><relatesTo>1</relatesTo><i> lcma@nuu.edu.tw</i>
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  Data: <searchLink fieldCode="JN" term="%22Computers+%26+Industrial+Engineering%22">Computers & Industrial Engineering</searchLink>. May2019, Vol. 131, p320-326. 7p.
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  Data: <searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink><br /><searchLink fieldCode="DE" term="%22Group+decision+making%22">Group decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+users%22">Computer users</searchLink>
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  Label: Abstract
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  Data: • A total ranking list involving group consensus preferences is achieved. • A group consensus mining approach is proposed without candidate generation. • An optimization model involving maximum consensus sequences is developed. • Flexibility is provided in solving ranking problems using different input formats. • Minimum consensus levels and maximum disagreement levels are adjustable. Discovering the group priority from a set of user preferences plays an important role in group decision making because of its extensive applications in practice. In most group ranking problems, users are supposed to input a ranking of alternatives regardless of the intensities of preference. However, if two users specify that they prefer A to B, the intensities of preference may be quite different. In addition, most researchers have tried to determine a total ranking list by minimizing total differences among user preferences, but users might have little consensus on the final results. This study aims to propose a new consensus-based approach for group ranking problems involving different intensities of preference. Stemming from the concept of consensus mining, consensus relationships are discovered by three accumulation matrices and consensus thresholds. An optimization model incorporating the consensus relationships and the concept of Borda majority count is then developed to derive a total ranking list. Compared to previous studies, the proposed approach can treat group ranking problems involving different intensities of preference, reduce the occurrence of ties, and achieve a total ranking list reflecting the consensus preference of the majority of users. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Computers & Industrial Engineering is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1016/j.cie.2019.04.001
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      – Code: eng
        Text: English
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        PageCount: 7
        StartPage: 320
    Subjects:
      – SubjectFull: Data mining
        Type: general
      – SubjectFull: Problem solving
        Type: general
      – SubjectFull: Group decision making
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Computer users
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      – TitleFull: A new consensus mining approach to group ranking problems involving different intensities of preferences.
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              Text: May2019
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              Y: 2019
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