Case-based classification model based on information diffusion and interval gray relational analysis

Yang, Banghua; Jiang, Junjie; Zhao, Jinshuai

doi:10.1108/gs-08-2020-0115

Cited by 3 publications

(4 citation statements)

References 31 publications

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“…In another study related to GIA methods, Yang et al . developed a grey relational model that incorporates information diffusion to address the issue of rank reversal when faced with limited or changing decision information (2022). The researchers devised an ideal point diffusion method and, using a virtual-ideal sequence, constructed a grey relational model for sample classification.…”

Section: Literature Reviewmentioning

confidence: 99%

The evaluation of grey relative incidence

Nowak,

Pawłowska-Nowak,

Kokocińska

et al. 2023

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PurposeWith the use of the grey incidence analysis (GIA), indicators such as the absolute degree of grey incidence (εij), relative degree of grey incidence (rij) or synthetic degree of grey incidence (ρij) are calculated. However, it seems that some assumptions made to calculate them are arguable, which may also have a material impact on the reliability of test results. In this paper, the authors analyse one of the indicators of the GIA, namely the relative degree of grey incidence. The aim of the article was to verify the hypothesis: in determining the relative degree of grey incidence, the method of standardisation of elements in a series significantly affects the test results.Design/methodology/approachTo achieve the purpose of the article, the authors used the numerical simulation method and the logical analysis method (in order to draw conclusions from our tests).FindingsIt turned out that the applied method of standardising elements in series when calculating the relative degree of grey incidence significantly affects the test results. Moreover, the manner of standardisation used in the original method (which involves dividing all elements by the first element) is not the best. Much more reliable results are obtained by a standardisation that involves dividing all elements by their arithmetic mean.Research limitations/implicationsLimitations of the conducted evaluation involve in particular the limited scope of inference. This is since the obtained results referred to only one of the indicators classified into the GIA.Originality/valueIn this article, the authors have evaluated the model of GIA in which the relative degree of grey incidence is determined. As a result of the research, the authors have proposed a recommendation regarding a change in the method of standardising variables, which will contribute to obtaining more reliable results in relational tests using the grey system theory.

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Section: Literature Reviewmentioning

confidence: 99%

The evaluation of grey relative incidence

Nowak,

Pawłowska-Nowak,

Kokocińska

et al. 2023

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“…According to the deviation degree of interval grey numbers, Liu et al (2017) proposed a grey relational degree model. Although these relational models of interval grey numbers have been widely applied in the evaluation (Bi et al, 2020;Zhang and Luo, 2022;Chia and Yee, 2021) and decision-making (Zhou et al, 2020;Sun, 2020;Yang et al, 2022;, these models are only based on the approximate expression of the external characteristics of interval grey number, and not based on the scientific expression of the connotative information of interval grey number. Therefore, according to the grey immortal axiom, Li and Li (2022a) first put forward the concept of generalized greyness of interval grey number and built a greyness relational degree model based on the greyness distance between two interval grey numbers, which not only realize the scientific expression of interval grey number, but also extend the application range of the classical grey relational degree model.…”

Section: Introductionmentioning

confidence: 99%

“…, 2020; Zhang and Luo, 2022; Chia and Yee, 2021) and decision-making (Zhou et al. , 2020; Sun, 2020; Yang et al. , 2022; Zhang et al.…”

Section: Introductionmentioning

confidence: 99%

Grey angle cosine relational degree model based on generalized greyness of interval grey number

Zhang,

2023

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PurposeAim to the limitations of grey relational analysis of interval grey number, based on the generalized greyness of interval grey number, this paper tries to construct a grey angle cosine relational degree model from the perspective of proximity and similarity.Design/methodology/approachFirstly, the algorithms of the generalized greyness of interval grey number and interval grey number vector are given, and its properties are analyzed. Then, based on the grey relational theory, the grey angle cosine relational model is proposed based on the generalized greyness of interval grey number, and the relationship between the classical cosine similarity model and the grey angle cosine relational model is analyzed. Finally, the validity of the model in this paper is illustrated by the calculation examples and an application example of related factor analysis of maize yield.FindingsThe results show that the grey angle cosine relational degree model has strict theoretical basis, convenient calculation and is easy to program, which can not only fully utilize the information of interval grey numbers but also overcome the shortcomings of greyness relational degree model. The grey angle cosine relational degree is an extended form of cosine similarity degree of real numbers. The calculation examples and the related factor analysis of maize yield show that the model proposed in this paper is feasible and valid.Practical implicationsThe research results not only further enrich the grey system theory and method but also provide a basis for the grey relational analysis of the sequences in which the interval grey numbers coexist with the real numbers.Originality/valueThe paper succeeds in realizing the algorithms of the generalized greyness of interval grey number and interval grey number vector, and the grey angle cosine relational degree, which provide a new method for grey relational analysis.

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“…Yang et al [24] used the virtual ideal sequence construction for case text classification, and built a grey relational model based on information diffusion to avoid rank reversal when the decision-making information available is insufficient or the decision object changes. The results show that the grey relational model based on information diffusion and virtual ideal sequence can effectively avoid rank reversal.…”

Section: Introductionmentioning

confidence: 99%

Classification of Online Course Teaching Cases Based on an Improved Clustering Method

Li¹

2022

Int. J. Emerg. Technol. Learn.

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The classification and sharing of online course teaching cases based on students’ individual learning needs can help teachers save time and cost in preparation of lessons and enrich their teaching content, and what is more, it is in line with the digitalization trend of the modern data-driven education. Currently, there has been few research on the combination of new information technology with teaching case classification, and the research is not adequate on the existing algorithms for classification of online course teaching cases. Therefore, this paper takes the teaching cases of online open courses of finance as the object, and studies the classification method for online course teaching cases based on an improved clustering method. First, the text clustering problem of finance teaching cases was described, and the conversion process of the abstracts of the case text and the evaluation method for the importance of words to teaching cases were given. Then, the key sentences were extracted from the text of the finance teaching cases, and the related algorithm was introduced. After that, the clustering flow chart for long texts of finance teaching cases was shown, and the principle of the combined clustering algorithm for finance teaching cases with uneven distribution of long texts and short texts was described in detail. The experimental results verified the effectiveness of the proposed algorithm.

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Case-based classification model based on information diffusion and interval gray relational analysis

Cited by 3 publications

References 31 publications

The evaluation of grey relative incidence

The evaluation of grey relative incidence

Grey angle cosine relational degree model based on generalized greyness of interval grey number

Classification of Online Course Teaching Cases Based on an Improved Clustering Method

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