ECNN: evaluating a cluster-neural network model for city innovation capability
发表日期：2021年9月18日
作者： Jiaming Pei1, 2, Kaiyang Zhong3, Jinhai Li1, Jiyuan Xu1 & Xinyi Wang1
单位： 1. College of Computer Science and Technology, Taizhou University, China
2. College of Computing, Illinois Institute of Technology, USA
3. School of Economic Information Engineering, Southwestern University of Finance and Economics, China
期刊： Neural Computing and Applications
volume 34, pages12331–12343 (2022)
摘要： Innovation capability is a great driving force leading city development. It is also important to evaluate the innovation capability of a city for city development. In this paper, we propose an ECNN model to evaluate city innovation capability. This model studies innovation capability from the perspective of machine learning. Compared with the existing statistical methods, it is a novel model, to the best of our knowledge, to evaluate the city’s innovation capability in terms of machine learning. It overcomes the shortcomings of the original statistical methods for studying the relationship between indicators without considering the relationship between indicators and innovation capabilities. This model first clusters all samples, and the sample categories are marked as clusters. Second, the weight of each indicator is calculated by the entropy gain rate, and the total score is calculated by adding the weighted values of each indicator. To obtain more precise results, the neural network calculates the sample scores, which have the same score but belong to the cluster, with good clustering data as the training set. In this way, different clusters represent different innovation capabilities. Each sample has an innovation capability score. Therefore, the ECNN model has high practicability in evaluating the innovation capability of cities.
关键词： Innovation capability; Clustering; Neural network; Machine learning
DOI： https://doi.org/10.1007/s00521-021-06471-z
链接： https://link.springer.com/article/10.1007/s00521-021-06471-z
