Multi-decision tree prediction model for coal seam floor water inrush based on cost-sensitive theory

When predicting coal seam floor water inrush, the situation is generally divided into two states: safe state and water inrush state. The state data has non-equilibrium characteristics. The existing coal seam floor water inrush prediction models are mainly suitable for balanced data. In the context of processing unbalanced data sets, the results often show 'one-sided' phenomenon which means that the accuracy of safe state prediction is significantly higher than the accuracy of water inrush state, therefore the overall prediction performance is low. To address this problem, the multi-decision tree prediction model for coal seam floor water inrush based on cost-sensitive theory is established. In this model, each decision tree selects different water inrush factors as the root node of the single decision tree, and the node attribute selection criterion of single decision tree combines the cost-sensitive theory and Gini index, thus increasing the penalty for false prediction of water inrush data (minority of case