基于演化CatBoost算法的房价预测模型

Evolutionary CatBoost Based Housing Price Prediction Model

遗传规划算法采用函数变换将原有变量张成的空间映射到新的特征空间,通过遗传算子操作实现目标函数的最优化。影响房价波动的因素有很多,各影响因素与房价之间呈现复杂的非线性关系。文中提出了一种基于演化CatBoost算法的房价预测模型,将影响房价波动的各因素变量编码为遗传规划算法的终端变量,采用CatBoost算法作为基学习器构建适应度函数,针对房价预测的特点设计合理的遗传算子,在函数映射后的特征空间上实现目标函数的最优化,以提升预测模型的性能。实验结果表明,基于演化CatBoost算法的房价预测模型的预测性能优于传统的基于随机森林算法、支持向量机算法、自适应增强算法、极致梯度提升算法等的预测模型,能够更好地实现房价的预测,在相同条件下具有更高的预测准确度。

Genetic programming algorithm uses function transformation to map the space formed by the original variables to a new feature space,and optimizes the objective function through genetic operator operations.There are many factors that affect housing price fluctuations,and each influencing factor exhibits a complex nonlinear relationship with housing prices.This paper proposes an evolutionary CatBoost algorithm based housing price prediction model.Various factor variables that affect housing price fluctuations are encoded as terminal variables of the genetic programming algorithm.CatBoost algorithm is employed as the base learner to construct a fitness function,and reasonable genetic operators are designed according to the characteristics of hou-sing price prediction.The objective function is optimized in the feature space after function mapping to improve the performance of the prediction model.Experimental results show that the prediction performance of evolutionary CatBoost algorithm based housing price prediction model is superior to that of traditional prediction models based on random forest algorithm,support vector machine algorithm,adaptive enhancement algorithm,extreme gradient enhancement algorithm,etc.It can predict housing prices more accurately than the rivals under the same conditions.