A dimensionality reduction method for large-scale group decision-making using TF-IDF feature similarity and information loss entropy

We 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.

Automated summary

In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.