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论文类型：会议论文

发表时间：2015-05-19

收录刊物：EI、CPCI-S、Scopus

卷号：9441

页面范围：189-197

关键字：PM2.5; Symbolic regression; Model selection; Interestingness measures

摘要：As one of the main components of haze, topics with respect to PM2.5 are coming into people's sight recently in China. In this paper, we try to predict PM2.5 concentrations in Dalian, China via symbolic regression (SR) based on genetic programming (GP). During predicting, the key problem is how to select accurate models by proper interestingness measures. In addition to the commonly used measures, such as R-squared value, mean squared error, number of parameters, etc., we also study the effectiveness of a set of potentially useful measures, such as AIC, BIC, HQC, AICc and EDC. Besides, a new interestingness measure, namely Interestingness Elasticity (IE), is proposed in this paper. From the experimental results, we find that the new measure gains the best performance on selecting candidate models and shows promising extrapolative capability.