A Numerical Comparison of Iterative Algorithms for Inconsistency Reduction in Pairwise Comparisons

Mazurek, Jiří; Strzałka, Dominik; Kuras, Pawel

doi:10.1109/access.2021.3074274

Cited by 10 publications

(7 citation statements)

References 44 publications

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“…Cao et al in 2019 [36] constructed an early warning level model for public opinion using the analytic hierarchy process (AHP) and the fuzzy comprehensive evaluation method. The AHP process is one of the so-called multi-criteria decisionmaking methods and helps reduce inconsistency in the weights of the criteria selected, as Mazurek et al in 2021 and Koczkodaj et al in 1993 wrote about [37,38]. Some early warning methods can be imperfect, especially when it comes to predicting phenomena with high uncertainty and randomness, such as public opinion.…”

Section: Early Warning Methods and Threat Reporting On The Example Of...mentioning

confidence: 99%

Threats and Crisis Events Detection Using Machine Learning Techniques With Social Media Data

ORGANIŚCIAK,

KURAŚ,

KOWAL

2023

Zeszyty Naukowe WST

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In the paper, the authors present the outcome of web scraping software allowing for the automated classification of threats and crisis events detection. In order to improve the safety and comfort of human life, an analysis was made to quickly detect threats using a modern information channel such as social media. For this purpose, social media services that are popular in the examined region were reviewed and the appropriate ones were selected using the criteria of accessibility and popularity. Approximately 300 unique posts from local groups of cities and other administrative centers were collected and analyzed. The decision of which entry was classified as a threat was defined using the ChatGPT tool and the human expert. Both variants were tested using machine learning (ML) methods. The paper tested whether the ChatGPT tool would be effective at detecting presumed events and compared this approach to the classic ML approach.

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Section: Early Warning Methods and Threat Reporting On The Example Of...mentioning

confidence: 99%

Threats and Crisis Events Detection Using Machine Learning Techniques With Social Media Data

ORGANIŚCIAK,

KURAŚ,

KOWAL

2023

Zeszyty Naukowe WST

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“…In the developed matrix generation method, the algorithms of Szybowski [12] and the algorithm of Xu and Wei [6] will be used, since their effectiveness has been demonstrated in a recent compilation of different techniques of this type, see Mazurek et. al [15], which also describes the performance of these algorithms in detail.…”

Section: 𝐶𝐶𝐶𝐶 =mentioning

confidence: 99%

Using Inconsistency Reduction Algorithms in Comparison Matrices to Improve the Performance of Generating Random Comparison Matrices with a Given Inconsistency Coefficient Range

Kuraś¹,

Gerka²

2023

Adv. Sci. Technol. Res. J.

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Recent decades have seen the development of many new multi-criteria decision-making methods, mainly through in-depth research on them. The main ones are techniques such as PRO-METHEE, PAPRIKA, VIKOR, ELECTREE, and especially BWM (Best-Worst Method) and AHP (Analytic Hierarchy Process, see e.g. Mazurek [1], Liang et al. [2], Saaty [3,4], Brans et al. [17,18], Hansen et al. [25], Opricovic et al. [26] and Alkihairi et al. [27].All these methods use pairwise comparison matrices, which are used also in such many other fields, see e.g. Koczkodaj et al. [20,21,22,23], Cavallo et al [24]. Since the authors of this paper, have repeatedly participated in research on methods that use them, they have also confronted the problem of efficiently generating random comparison matrices -necessary, for example, to perform Monte Carlo simulations for such methods that require large amounts of random data, see e.g. Caflisch [5]. This problem was particularly evident in the case of matrices, the so-called "large" (from 6x6 to 10x10) and when one wanted to simultaneously obtain a low CR -consistency range (especially below the value of 0.1). Then the generation times for several thousand random matrices with the desired parameters could reach even several days. This was a big obstacle for performing efficient research, as the need to examine large amounts of data with a specific consistency interval may be needed on such fronts as the study of constructive consistent approximations

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“…While there are many algorithms for this purpose, in principle there are two groups of pairwise comparison matrix inconsistency reduction algorithms -non-iterative and iterative [21]. Based on the conclusions of the research work by the team of Mazurek et al [20], the library proposed in this paper includes algorithms from the iterative algorithm group. These are the algorithms by Cao et al [27], Szybowski [28] and Xu and Wei [29].…”

Section: Pairwise Comparisonsmentioning

confidence: 99%

REDUCE – A Python Module for Reducing Inconsistency in Pairwise Comparison Matrices

Kuraś¹,

Strzałka²,

Mazurek³

2023

Adv. Sci. Technol. Res. J.

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This paper introduces REDUCE, a Python module designed to minimize inconsistency in multiplicative pairwise comparisons (PC), a fundamental technique in Multi-Criteria Decision Making (MCDM). Pairwise comparisons are extensively used in various fields, including engineering science and numerical simulation methods, to compare different options based on a set of criteria. However, human errors in perception and judgment often lead to inconsistencies in pairwise comparison matrices (PCM). REDUCE addresses this issue by implementing several algorithms that identify and correct inaccurate data in PCMs, thereby reducing the inconsistency ratio. These algorithms do not require expert intervention, making REDUCE a valuable tool for both scientific research and small to medium enterprises that may not have access to costly commercial software or dedicated decision-making experts. The main functionality of the module is incorporating iterative algorithms for inconsistency reduction. The REDUCE library, written in Python and utilizing auxiliary libraries such as NumPy, SciPy, and SymPy, offers 21 functions categorized into data input helpers, consistency ratio (CR) reduction algorithms, PCM indexes, and support functions. Performance testing indicates that the library can efficiently handle matrices of varying sizes, particularly those ranging from 3x3 to 10x10, and its use significantly accelerates the process compared to spreadsheets, especially when dealing with large quantities of matrices. The library has already been used in several research papers and application tools, and its availability as a free resource opens up opportunities for small and medium-sized enterprises to leverage multi-criteria decision-making methods. Currently, there are no publicly available libraries for this solution. The authors believe that the proposed module may contribute to do better decision-making process in pairwise comparisons, not only for the circle of scientists but also for small and medium enterprises that usually cannot afford expensive commercial software and do not employ full-time experts in decision-making as they rely on the experience of their employees and free online resources. It should also contributes to the transition to Industry 4.0 and advances research in fields such as fuzzy logic, preference programming, and constructive consistent approximations.

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A Numerical Comparison of Iterative Algorithms for Inconsistency Reduction in Pairwise Comparisons

Cited by 10 publications

References 44 publications

Threats and Crisis Events Detection Using Machine Learning Techniques With Social Media Data

Threats and Crisis Events Detection Using Machine Learning Techniques With Social Media Data

Using Inconsistency Reduction Algorithms in Comparison Matrices to Improve the Performance of Generating Random Comparison Matrices with a Given Inconsistency Coefficient Range

REDUCE – A Python Module for Reducing Inconsistency in Pairwise Comparison Matrices

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