A supervised learning approach to dynamic weighted fusion in multi-source ordered decision systems

With the rapid advancement of new-generation artificial intelligence technologies, machines can process and analyze large-scale data more accurately and efficiently and for more complex tasks. Enhancing the usability and value of the information derived from various information systems across multiple dimensions is essential. However, traditional data dominance relationships cannot reflect people’s different levels of attention to antithetic features, leading to higher complexity and lower classification accuracy. Therefore, it is necessary to consider the weight relationships between attributes in the data, which refers to the degree of correlation between each attribute and the decision in multi-source information systems. Based on these weights and dominance relationships, we consider an entropy-based weighted information fusion method for processing supervised data in multi-source ordered decision systems. We intend four incremental fusion mechanisms to adjust information sources and attribute changes to save running time. Furthermore, experiments are conducted on nine real datasets to demonstrate our method’s effectiveness. The results show that the inevitable accuracy comparisons by the proposed method are superior to most fusion methods. In addition, the dynamic mechanisms, compared to static mechanisms, can significantly reduce running time.