RFR-ABROF: A Multi-Strategy Collaborative Classification Prediction Model Based on Rotation Forest for PM2.5

Predicting dust pollution is necessary to achieve good air quality and patient recovery. In the era of big data, there are already many machine learning algorithms for predicting the concentration of air pollutant PM2.5. However, these model methods perform poorly when dealing with massive air quality data sets and do not solve the impact of data distribution imbalance. Therefore, a multi-strategy collaborative feature selection method combining random forests and recursive feature elimination with adaptive boosting rotation forest (RFR-ABROF) algorithm is proposed. On this basis, multiclass adaptive synthetic sampling (Multi-ADASYN) strategy is introduced to balance the imbalanced air quality data set. We verified the effectiveness of the model through comparative experiments, which show our proposed model has the higher values of accuracy, precision, recall, f1-score, and ROC curve area under evaluation indicators in the air quality data set of four locations in most cases, and the more considerable the amount of data, the better the model prediction performance.