A new oversampling method and improved radial basis function classifier for customer consumption behavior prediction

Yue Li, Xiaoyun Jia, Ruili Wang, Jianfang Qi, Haibin Jin, Xiaoquan Chu, Weisong Mu

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Abstract

In practical applications, imbalanced data has brought great challenges to classification problems. In this paper, we propose two new methods: (1) a new oversampling method, named Tomek-CASUWO, to address the issue of class imbalance; (2) a new classifier, named ILS-RBFNN, to increase the accuracy of prediction of customer consumption behavior. Since current ASUWO algorithm is easily affected by noise samples and fuzzy class boundaries, we propose Tomek-CASUWO to address these problems: (i) the Tomek links algorithm is used to filter noise samples; (ii) CASUWO is used to avoid overlapping class boundaries; (iii) Tomek-CASUWO is used to synthesize new samples. Also, we propose a new classifier based on RBFNN, named ILS-RBFNN, to improve the prediction accuracy: (i) the hybrid kernel is developed by combining Gaussian and Polynomial; (ii) an Immune Algorithm (IA) is used to optimize the centers of RBFNN; (iii) Least-Squares (LS) is used to calculate the biases and weights. Wine-consumer behavior data is used to compare our Tomek-CASUWO with other oversampling methods. We compare ILS-RBFNN with several well-known kernel functions and parameter update methods. The experimental results show that Tomek-CASUWO can significantly improve the prediction accuracy of a classifier, and ILS-RBFNN outperforms other classification methods. We also conduct experiments on the extended real-world dataset. Finally, the robustness and applicability of ILS-RBFNN are verified on the eleven UCI datasets. All results show that the proposed two methods outperform existing models. The experimental results also demonstrate the effectiveness and practicability of ILS-RBFNN for predicting customer behavior.

Original languageEnglish
Article number116982
JournalExpert Systems with Applications
Volume199
DOIs
Publication statusPublished - 1 Aug 2022
Externally publishedYes

Keywords

  • Consumer consumption behavior prediction
  • Imbalanced data
  • Immune algorithm
  • Oversampling technique
  • RBF neural network

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence

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Li, Y., Jia, X., Wang, R., Qi, J., Jin, H., Chu, X., & Mu, W. (2022). A new oversampling method and improved radial basis function classifier for customer consumption behavior prediction. Expert Systems with Applications, 199, Article 116982. https://doi.org/10.1016/j.eswa.2022.116982