An Integrated Attribute-Weighting Method Based on PCA and Entropy: Case of Study Marginalized Areas in a City

Odette Pliego-Martinez,Alicia Martinez-Rebollar,Hugo Estrada-Esquivel, Ernesto de la Cruz-Nicolas

APPLIED SCIENCES-BASEL(2024)

引用 0|浏览0
暂无评分
摘要
The precise allocation of weights to criteria plays a fundamental role in multicriteria decision-making, exerting a significant influence on the obtained results. Ensuring an appropriate weighting of criteria is crucial for conducting a fair and accurate evaluation of various alternatives. In this context, we present an innovative solution that addresses the allocation of weights to attributes in datasets, aiming to overcome limitations and challenges associated with expert consultation in multicriteria problems. The proposed method is grounded in an objective approach and adopts a hybrid perspective by integrating the mathematical principles of Principal Component Analysis with the application of the Entropy Method. This method was implemented along with the exponential weighted sum model in a case study related to the classification of neighborhoods in Mexico City based on the level of marginalization. Results were compared with the marginalization index reported in official sources, using evaluation metrics MAE and MAPE with values of 0.24 and 11.3%, respectively. This research demonstrates the efficiency of the proposed method, which integrates techniques used for attribute weighting, providing a robust and reliable tool for decision-making.
更多
查看译文
关键词
multiple-attribute decision-making,weighting of criteria,PCA,entropy
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要