A dimensionality reduction method for large-scale group decision-making using TF-IDF feature similarity and information loss entropy
2023; Elsevier BV; Volume: 150; Linguagem: Inglês
10.1016/j.asoc.2023.111039
ISSN1872-9681
AutoresQifeng Wan, Xuanhua Xu, Jing Han,
Tópico(s)Rough Sets and Fuzzy Logic
ResumoWe introduce an innovative approach for dimensionality reduction targeting linguistic preferences in large-scale group decision-making scenarios. This method combines TF-IDF feature similarity and information loss entropy to address the challenges of decision-making over large-scale decision makers. Firstly, text vectorization is performed to capture the semantics of the text as a TF-IDF feature matrix, which facilitates subsequent calculations. Secondly, a cluster process integrating the TF-IDF feature similarity is operated to divide the large-scale decision-maker group into several clusters. Thirdly, the selection process is activated to select representatives from among the large-scale decision-makers based on information loss entropy. Finally, a case study was conducted to test the practical feasibility of the proposed method, along with a comparative analysis to discuss the scenarios in which it is applicable.
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