Establishing A Hybrid Pieceswise Linear Model for Air Quality Prediction Based Missingness Challenges

Air pollution has threatened people's health. It is urgent for the government to strengthen and improve the ability of air pollution monitoring. This paper proposes a winner-take-all (WTA) autoencoder-based piecewise linear model for imputing air quality prediction under the missing data scenario. The main idea consists of two parts. Firstly, overcomplete WTA stacked denoising autoencoders (SDAEs) are proposed to handle missing data, which play two roles: 1) devise the multiple imputation strategy to fill in missing values; 2) generate a set of binary gate control sequences to construct the sophisticated partitioning. Besides, renewed teacher signals are updated based on clustering information in the trained SDAEs to improve the accuracy of filling in missing samples. Secondly, the piecewise linear model is then proposed by the generated set of gate signals using the information from the feature layers of SDAEs. By using a quasi-linear kernel based on the trained gating mechanism, our piecewise air quality predictor is finally identified in the exact same way as support vector regression. The proposed modeling method is applied to real air quality datasets to show that it has led to greater performance than traditional models.