Evaluation of Economics Journals Based on Reduction Algorithm of Rough Set and Grey Correlation

Huang, Mei-Jia; Zhang, Yuanbiao; Luo, Jie-Huan; He, Ning

doi:10.5539/jms.v5n1p140

Cited by 3 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A decision model was built for ranking the journals using TOPSIS method by applying two-weight calculation methods, namely Rough set approach [15] and Grey correlation method [16]. Grey Relational Analysis (GRA) is a popular MCDM method used for decision making when multiple conflicting criteria are involved.…”

Section: Related Workmentioning

confidence: 99%

Multiclass Model for Agriculture Development Using Multivariate Statistical Method

et al. 2020

View full text Add to dashboard Cite

Mahalanobis taguchi system (MTS) is a multi-variate statistical method extensively used for feature selection and binary classification problems. The calculation of orthogonal array and signalto-noise ratio in MTS makes the algorithm complicated when more number of factors are involved in the classification problem. Also the decision is based on the accuracy of normal and abnormal observations of the dataset. In this paper, a multiclass model using Improved Mahalanobis Taguchi System (IMTS) is proposed based on normal observations and Mahalanobis distance for agriculture development. Twentysix input factors relevant to crop cultivation have been identified and clustered into six main factors for the development of the model. The multiclass model is developed with the consideration of the relative importance of the factors. An objective function is defined for the classification of three crops, namely paddy, sugarcane and groundnut. The classification results are verified against the results obtained from the agriculture experts working in the field. The proposed classifier provides 100% accuracy, recall, precision and 0% error rate when compared with other traditional classifier models.

show abstract

Section: Related Workmentioning

confidence: 99%

Multiclass Model for Agriculture Development Using Multivariate Statistical Method

et al. 2020

View full text Add to dashboard Cite

show abstract

Section: Related Workmentioning

confidence: 99%

Multiclass Model for Agriculture development using Multivariate Statistical method

Deepa,

Khan,

et al. 2020

Preprint

View full text Add to dashboard Cite

Mahalanobis taguchi system (MTS) is a multivariate statistical method extensively used for feature selection and binary classification problems. The calculation of orthogonal array and signal-to-noise ratio in MTS makes the algorithm complicated when more number of factors are involved in the classification problem. Also the decision is based on the accuracy of normal and abnormal observations of the dataset. In this paper, a multiclass model using Improved Mahalanobis Taguchi System (IMTS) is proposed based on normal observations and Mahalanobis distance for agriculture development. Twenty-six input factors relevant to crop cultivation have been identified and clustered into six main factors for the development of the model. The multiclass model is developed with the consideration of the relative importance of the factors. An objective function is defined for the classification of three crops, namely paddy, sugarcane and groundnut. The classification results are verified against the results obtained from the agriculture experts working in the field. The proposed classifier provides 100% accuracy, recall, precision and 0% error rate when compared with other traditional classifier models.

show abstract

“…The objective weights of the criteria were calculated using the weight assignment method, namely Shannon's entropy method [8]. An evaluation model was developed to rank the economics journals by applying objective weight assignment method, namely, grey correlation method and technique for order of preference by similarity to ideal solution (TOPSIS) method [23]. An extended SWARA method was proposed to improve the weighting process in decision-making problems [24].…”

Section: Introductionmentioning

confidence: 99%

Realizing Sustainable Development via Modified Integrated Weighting MCDM Model for Ranking Agrarian Dataset

et al. 2019

View full text Add to dashboard Cite

One of the crucial elements in decision-making is the calculation of criteria weights. In this paper, a new Modified Integrated Weighting (MIW) method was proposed to combine the weights obtained using different weight calculation methods into a single set of weights. The weights express the relative significance of the criteria and play an essential role in making correct decisions. The proposed method considered both subjective knowledge of the experts and the objectivity of the problem by combining the subjective and objective weight assignment methods. The proposed weight calculation method was applied to the agriculture dataset for the evaluation of groundnut crop sites. A decision-making model was developed via the proposed MIW method and Complex Proportional Assessment (COPRAS) method to rank the given groundnut crop site dataset. The ranking results of the developed decision model were compared with the ranking results of average yield data and other methods for validation purposes. The developed model exhibited better results for the given dataset and could be used to solve various other decision-making problems, thereby realizing sustainable development.

show abstract

Evaluation of Economics Journals Based on Reduction Algorithm of Rough Set and Grey Correlation

Cited by 3 publications

References 1 publication

Multiclass Model for Agriculture Development Using Multivariate Statistical Method

Multiclass Model for Agriculture Development Using Multivariate Statistical Method

Multiclass Model for Agriculture development using Multivariate Statistical method

Realizing Sustainable Development via Modified Integrated Weighting MCDM Model for Ranking Agrarian Dataset

Contact Info

Product

Resources

About