Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 240300180-5.doi: 10.11896/jsjkx.240300180

• Interdiscipline & Application • Previous Articles     Next Articles

Evolutionary CatBoost Based Housing Price Prediction Model

WANG Chengzhang1, BAI Xiaoming2, TANG Wenying1, CHEN Shuhan1   

  1. 1 School of Statistics and Mathematics,Central University of Finance and Economics,Beijing 100081,China
    2 School of Management and Engineering,Capital University of Economics and Business,Beijing 100070,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:WANG Chengzhang,born in 1977,Ph.D,associate professor,master supervisor.His main research interests include machine learning,pattern recognition and big data analysis.
  • Supported by:
    Social Science Foundation of Beijing(21GGB018) and the Educational Reform Foundation of Central University of Finance and Economics(2022ZXJG09).

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Abstract: 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.

Key words: Genetic programming, CatBoost algorithm, Prediction model, Decision tree, Optimization

CLC Number: 

  • TP391
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