A multidimensional power material supply chain data classification method based on trusted similarity

In order to improve the efficiency of data management, ensure data security, and optimize resource allocation, a multi-dimensional power supply chain data classification method based on trusted similarity is proposed. Calculate the credibility of the data type and the credibility of the data score, taking into account both the credibility of the data type and the credibility of the score, to obtain the credibility similarity of the data. Using the random forest algorithm combined with trusted similarity for data classification, in the random forest algorithm, trusted similarity is added as an additional feature to the dataset. This feature can help algorithms better understand the relationships between data. In addition to serving as a feature, trustworthy similarity can also be used to adjust the weights of other features. Optimize and further adjust the linear function through cross validation to achieve the best classification performance. The experimental results show that the proposed method has a low information loss rate, high classification accuracy, and low time consumption in data classification, indicating that it can provide strong support for the optimization and management of the power supply chain.