Abstract
City innovative capability analysis and prediction play an important role in regional innovation systems development and improve benefit of innovative capability for country. According to the city innovative capability data which is large scale and imbalance, this paper presented a support vector machine model to predict city innovative capability. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding city innovative capability prediction for thirteen 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 innovative capability classification and prediction.
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