A NEURAL-ENSEMBLE LEARNING METHOD FOR MIGRATION PREDICTION BASED ON CULINARY TASTE DATA IN CHINA

Population migration is an important problem related to national economic and social development, and migration data can be applied to research in many fields. But population loss often puts huge pressure on local governments, so migration data are not disclosed in many cases. Most of the existing migration prediction models are based on non open source data, when other researchers want to apply existing population migration prediction models to carry out their own prediction tasks, they often find that they cannot obtain the same data source. This paper proposes a Neural -Ensemble learning method for migration prediction based on taste data in China. The whole method can be divided into three parts. First, classify the restaurants into different cuisines, calculate the taste of each cuisine based on the recipe data and then obtain the taste matrix of China. In this step, we propose a method for restaurant classification called Neural -Ensemble Classification, which combines the BERT and dictionary matching. Then we construct a Markov Chain to predict the vector of migration at the same time with restaurant data based on the historical migration data. Finally, we build a prediction model based on the LightGBM, which uses the taste matrix as input and the vector of migration as output. Compared with existing models, this model can use open data to achieve the prediction accuracy no lower than existing models.