Expert Credibility Prediction Model Based on Fuzzy C-Means Clustering and Similarity Association

In the current wave of technological innovation, the scientific and technological evaluation system not only plays a crucial role in assessing scientific research achievements but also serves as a driving force for advancing scientific research. To enhance the effectiveness and accuracy of peer review in the scientific and technological evaluation system, an integrating fuzzy C-Means clustering algorithm and similarity analysis method is proposed. In this methodology, the fuzzy C-Means clustering algorithm is first applied to conduct cluster analysis on expert feature attribute. Second, the recursive feature elimination feature selection is used to select the important features for enhancing the model generalization capability. The Gower distance of key features between experts is calculated to determine the similarity between experts, and the association features among experts are mined. Finally, an expert credibility prediction model is constructed based on a feedforward neural network. A real-world expert credibility dataset was used to validate the effectiveness and feasibility of the proposed method. The experimental results indicated that integrating the fuzzy C-Means clustering algorithm and similarity association is an effective way to improve the model prediction performance. This can be considered a promising solution for the scientific and technological evaluation.