Predicting technology of gas emission quantity in coal mine based on enhanced CART regression algorithm

From the view of data mining, predicting gas emission quantity from mining coal face is a kind of regression problem and major predicting algorithms include CART (classification and regression tree) and SVM (Support vector machine), and so on.CART has some inherent advantages, such as simple extraction rule, high accuracy and strong interpretability.However, CART is poor in stability and easily over-fitting.Moreover, the output of each leaf node is constant, which makes it difficult to dynamically simulate the variation law of real data.On the other side, SVM has good robustness and can improve the generalization ability by solving the minimum structural risk while its computation complexity is high and its efficiency is low when facing high-dimensional data.Combined with SVM, this article proposes an enhanced CART regression algorithm, of which the critical change exists in adopting SVM in each leaf node for model building.The experimental results show that the enhanced CART regression algorithm effectively improve the prediction accuracy of gas emission quantity from coal mining face.