An Improved Genetic-XGBoost Classifier for Customer Consumption Behavior Prediction

• Published in: Computer journal Vol. 67; no. 3; pp. 1041 - 1059
• Main Authors: Li, Yue, Qi, Jianfang, Jin, Haibin, Tian, Dong, Mu, Weisong, Feng, Jianying
• Format: Journal Article
• Language: English
• Published: Oxford University Press 14.04.2024
• Subjects: XGBoost algorithm; Lasso model; consumption behavior prediction; feature selection; genetic mechanism
• ISSN: 0010-4620; 1460-2067
• DOI: 10.1093/comjnl/bxad041

In an increasingly competitive market, predicting the customer’s consumption behavior has a vital role in customer relationship management. In this study, a new classifier for customer consumption behavior prediction is proposed. The proposed methods are as follows: (i) A feature selection method based on least absolute shrinkage and selection operator (Lasso) and Principal Component Analysis (PCA), to achieve efficient feature selection and eliminate correlations between variables. (ii) An improved genetic-eXtreme Gradient Boosting (XGBoost) for customer consumption behavior prediction, to improve the accuracy of prediction. Furthermore, the global search ability and flexibility of the genetic mechanism are used to optimize the XGBoost parameters, which avoids inaccurate parameter settings by manual experience. The adaptive crossover and mutation probabilities are designed to prevent the population from falling into the local extremum. Moreover, the grape-customer consumption behavior dataset is employed to compare the six Lasso-based models from the original, normalized and standardized data sources with the Isometric Mapping, Locally Linear Embedding, Multidimensional Scaling, PCA and Kernel Principal Component Analysis methods. The improved genetic-XGBoost is compared with several well-known parameter optimization algorithms and state-of-the-art classification approaches. Furthermore, experiments are conducted on the University of California Irvine datasets to verify the improved genetic-XGBoost algorithm. All results show that the proposed methods outperform the existing ones. The prediction results provide the decision-making basis for enterprises to formulate better marketing strategies.