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Entropy
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Application of Entropy in Decision-Making
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Application of Entropy in Decision-Making

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 4196

Special Issue Editors

Prof. Dr. Gwo-Hshiung Tzeng

E-Mail Website
Guest Editor
Distinguished Chair Professor, College of Public Affairs, National Taipei University, New Taipei City 23741, Taiwan
Interests: mutiple criteria decisoin making; decision making trial and evaluation laboratory (DEMATEL); ANP (analytic network process) and AHP (analytic hirachy process)
Special Issues, Collections and Topics in MDPI journals
Dr. Sun-Weng Huang

E-Mail Website
Guest Editor
Department of Marketing and Logistics, China University of Technology, New Taipei City 116, Taiwan
Interests: applications and innovations of diverse hybrid multiple criteria-decision-making (MCDM) methods; fuzzy decision-making; group decision making
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, entropy is one of the most important topics in various fields for achieving proper and sufficient decisions. This research approach has been considered a popular method and contributes significantly in both practical and academic aspects. With the application of entropy, the decisions were mainly based on the dispersion degree of information; certainly, the information, hidden in the data, could be revealed effectively and objectively; nevertheless, in the real world, the decision-making process tends to be complicated; it is considerably arduous to quantify the information in an objective manner. Meanwhile, instead of adopting the sole research method, a hybrid approach, combining entropy with other methods, was widely conducted. Furthermore, for implementing the non-additive decisions, the expert system has been confirmed as one of the feasible and efficacious optimal solutions. However, the issue of the biasedness and objectivity of information should be further addressed. In recent years, the question of how to optimize the decision-making problems with the comprehensive consideration both objectively and subjectively has attracted academic and practice attentions and become a novel development direction in the field of decision-making research.

This Special Issue aims to collect quality papers on the development or application of Multi-Attribute Decision-making with Entropy for optimization models in the diverse fields. With this Special Issue, we aspire to make significant contributions on advances in the entropy literature from different operational and theoretical aspects. Submitted papers should not have been previously published or be currently under consideration for publication elsewhere.

We invite authors to submit original research articles that propose novel hybrid optimization models for solving decision-making problems in distinct fields.

Prof. Dr. Gwo-Hshiung Tzeng
Dr. Sun-Weng Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • application of entropy methods
  • application of uncertainty entropy approaches
  • hybrid decision-making analysis with application of entropy approaches
  • applications of entropy methods for optimization
  • innovative applications of entropy methods
  • application of multi-attributes decision-making and entropy

Published Papers (3 papers)

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Research

19 pages, 717 KiB  
Article
An Ensemble Outlier Detection Method Based on Information Entropy-Weighted Subspaces for High-Dimensional Data
by Zihao Li and Liumei Zhang
Entropy 2023, 25(8), 1185; https://doi.org/10.3390/e25081185 - 09 Aug 2023
Cited by 2 | Viewed by 1034
Abstract
Outlier detection is an important task in the field of data mining and a highly active area of research in machine learning. In industrial automation, datasets are often high-dimensional, meaning an effort to study all dimensions directly leads to data sparsity, thus causing [...] Read more.
Outlier detection is an important task in the field of data mining and a highly active area of research in machine learning. In industrial automation, datasets are often high-dimensional, meaning an effort to study all dimensions directly leads to data sparsity, thus causing outliers to be masked by noise effects in high-dimensional spaces. The “curse of dimensionality” phenomenon renders many conventional outlier detection methods ineffective. This paper proposes a new outlier detection algorithm called EOEH (Ensemble Outlier Detection Method Based on Information Entropy-Weighted Subspaces for High-Dimensional Data). First, random secondary subsampling is performed on the data, and detectors are run on various small-scale sub-samples to provide diverse detection results. Results are then aggregated to reduce the global variance and enhance the robustness of the algorithm. Subsequently, information entropy is utilized to construct a dimension-space weighting method that can discern the influential factors within different dimensional spaces. This method generates weighted subspaces and dimensions for data objects, reducing the impact of noise created by high-dimensional data and improving high-dimensional data detection performance. Finally, this study offers a design for a new high-precision local outlier factor (HPLOF) detector that amplifies the differentiation between normal and outlier data, thereby improving the detection performance of the algorithm. The feasibility of this algorithm is validated through experiments that used both simulated and UCI datasets. In comparison to popular outlier detection algorithms, our algorithm demonstrates a superior detection performance and runtime efficiency. Compared with the current popular, common algorithms, the EOEH algorithm improves the detection performance by 6% on average. In terms of running time for high-dimensional data, EOEH is 20% faster than the current popular algorithms. Full article
(This article belongs to the Special Issue Application of Entropy in Decision-Making)
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21 pages, 808 KiB  
Article
The Impact of the Intuitionistic Fuzzy Entropy-Based Weights on the Results of Subjective Quality of Life Measurement Using Intuitionistic Fuzzy Synthetic Measure
by Ewa Roszkowska, Marzena Filipowicz-Chomko, Marta Kusterka-Jefmańska and Bartłomiej Jefmański
Entropy 2023, 25(7), 961; https://doi.org/10.3390/e25070961 - 21 Jun 2023
Cited by 1 | Viewed by 961
Abstract
In this paper, an extended Intuitionistic Fuzzy Synthetic Measure (IFSM) with intuitionistic fuzzy (IF) entropy-based weights is presented. This method can be implemented in a ranking problem where the assessments of the criteria are expressed in the form of intuitionistic fuzzy values and [...] Read more.
In this paper, an extended Intuitionistic Fuzzy Synthetic Measure (IFSM) with intuitionistic fuzzy (IF) entropy-based weights is presented. This method can be implemented in a ranking problem where the assessments of the criteria are expressed in the form of intuitionistic fuzzy values and the information about the importance criteria is unknown. One example of such a problem is measuring the subjective quality of life in cities. We join the debate on the determination of weights for the analysis of the quality of life problem using multi-criteria methods. To handle this problem, four different IF entropy-based weight methods were applied. Their performances were compared and analyzed based on the questionnaires from the survey concerning the quality of life in European cities. The studies show very similar weighting systems obtained by different IF entropy-based approaches, resulting in almost the same city rankings acquired through IFSM by using those weights. The differences in rankings obtained through the IFSM measure (and only by one position) concern the six cities included in the analysis. Our results support the assumption of the equal importance of the criteria in measuring this complex phenomenon. Full article
(This article belongs to the Special Issue Application of Entropy in Decision-Making)
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17 pages, 580 KiB  
Article
Outlier Detection with Reinforcement Learning for Costly to Verify Data
by Michiel Nijhuis and Iman van Lelyveld
Entropy 2023, 25(6), 842; https://doi.org/10.3390/e25060842 - 25 May 2023
Viewed by 1656
Abstract
Outliers are often present in data and many algorithms exist to find these outliers. Often we can verify these outliers to determine whether they are data errors or not. Unfortunately, checking such points is time-consuming and the underlying issues leading to the data [...] Read more.
Outliers are often present in data and many algorithms exist to find these outliers. Often we can verify these outliers to determine whether they are data errors or not. Unfortunately, checking such points is time-consuming and the underlying issues leading to the data error can change over time. An outlier detection approach should therefore be able to optimally use the knowledge gained from the verification of the ground truth and adjust accordingly. With advances in machine learning, this can be achieved by applying reinforcement learning on a statistical outlier detection approach. The approach uses an ensemble of proven outlier detection methods in combination with a reinforcement learning approach to tune the coefficients of the ensemble with every additional bit of data. The performance and the applicability of the reinforcement learning outlier detection approach are illustrated using granular data reported by Dutch insurers and pension funds under the Solvency II and FTK frameworks. The application shows that outliers can be identified by the ensemble learner. Moreover, applying the reinforcement learner on top of the ensemble model can further improve the results by optimising the coefficients of the ensemble learner. Full article
(This article belongs to the Special Issue Application of Entropy in Decision-Making)
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