A computing method for attribute importance based on BP neural network

As an important method for machine learning, artificial neural network has been applied successfully in artificial intelligence, pattern recognition, image processing and other fields. As the essence of neural network learning, BP network utilizes the error back propagation to correct weights continually in order to achieve the best-fit. The multi-attribute decision-making problem is a hotspot in decision theory. When involving multiple attributes, it needs to analyze the importance degrees for different attributes, i.e., weights of attributes.According to the correlation and importance problems of multiple input attributes for multi-classification output results, an importance method for calculating complex input attributes based on BP neural network was proposed. In addition, the BP neural network model for calculating the importance degrees of attributes was established through researching the number of nodes, the layers of network, learning strategies and learning factors in neural networks. The data of teaching evaluation of Yantai University is utilized to verify the feasibility and validity of the proposed method through applying k-fold approach.