City Innovation System Efficiency Prediction Based on Support Vector Machine: Taking Eight Chinese Cities as the Example

Abstract

Innovation system efficiency analysis and prediction play an important role in regional innovation systems development and improve benefit of innovative capacity for country. According to the city innovation system data which is large scale and imbalance, this paper presented a support vector machine model to predict city innovation system efficiency. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding city innovation system efficiency prediction for eight Chinese cities. It is found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient, and provides an effective measurement for city innovation system efficiency prediction.