关键词: 梯度提升决策树, 鲸鱼优化算法, 集成学习, 回归预测

Abstract: Aiming at the problem that it was difficult to select the parameters of gradient boosting decision tree (GBDT), we proposed a GBDT regression prediction algorithm based on improved whale optimization algorithm (IWOA). Firstly, an improved whale optimization algorithm was proposed, which initialized the population by using chaotic mapping to improve the diversity of the population, and the inertial weight and the mutation crossover strategy of differential evolution algorithm were introduced to solve the problem that it was easy to fall into the local optimization in the later stage of iteration. Secondly, IWOA was used to optimize the key parameters of the GBDT to avoid the blindness of parameter selection and improve the generalization ability of the regression prediction model. Finally,
 the IWOA-GBDT regression prediction model was established and verified by the UCI dataset. The experimental results show that compared with decision tree, support vector machine, Adaboost and GBDT algorithms, the proposed model algorithm has better fitting effect and certain practical value.

Key words: gradient boosting decision tree, whale optimization algorithm, ensemble learning, regression prediction