Entropy weight method (EWM) is a commonly used weighting method that measures value dispersion in decision-making. The greater the degree of dispersion, the greater the degree of differentiation, and more information can be derived. Meanwhile, higher weight should be given to the index, and vice versa.
The advantages of EWM:
The biggest advantage of the EWM is the avoidance of the interference of human factors on the weight of indicators, thus enhancing the objectivity of the com-prehensive evaluation results. Therefore, the EWM has been widely used in decision-making in recent years.
The disadvantages of EWM:
EWM only considered the numerical discrimination degree andignored the rank discrimination degree of the index.
The code had been written in EWM.py and the test data is in ewm.csv.
https://www.hindawi.com/journals/mpe/2020/3564835/
https://www.researchgate.net/publication/340232284_Effectiveness_of_Entropy_Weight_Method_in_Decision-Making
https://baike.baidu.com/item/%E7%86%B5%E6%9D%83%E6%B3%95/5616693?fr=aladdin
https://www.zhihu.com/question/357680646/answer/943628631
https://www.bilibili.com/video/bv1qt4y1276a