Multiattribute decision-making approach is one of the key complex system evaluation technologies which has attracted high attention of academic research studies. This paper establishes a novel multiattribute decision evaluation approach. First, we propose a high-dimensional data attribute reduction model based on partial correlation analysis and factor analysis methods. Second, based on the attribute weights calculated by multiple weighting methods, the corresponding multiple evaluation score vectors of the objects evaluated can be obtained. The final scoring vector can be determined by combining the quadratic combination weighting and the Spearman consistency test. Third, we use fuzzy C-means algorithm to grade evaluated objects. Finally, the established evaluation approach in this paper is verified by using the 107 observations in China. This approach also provides a decision-making example for attribute reduction of high-dimensional data, scoring of complex system evaluation, and clustering analysis when conducting evaluation in other fields.
Online Learning and Decision-Making under Generalized Linear Model with High-Dimensional Data