Complex-Valued GMDH-Based Data Characteristic-Driven Adaptive Decision Support System for Customer Classification

For real-world customer classification data, data structures are often highly uncertain. The constructed models may not match the data structure characteristics, which can lead to unsatisfactory classification performance. Further, the class distribution characteristics of data sets are usually highly imbalanced, which can also weaken the classification performance. To solve the above problems, we construct a complex-valued group method of data handling (CGMDH) neural network-based data characteristic-driven adaptive decision support system. First, we introduce the linearly separable discriminant method to analyze the data structure characteristics. Second, we extend the circular linear CGMDH neural network model and propose a circular quadratic nonlinear CGMDH (QCGMDH) neural network model. Finally, according to the data structure characteristics and resampling technique, we adaptively select and train the most appropriate CGMDH neural network model from two types of CGMDH. To analyze the effectiveness of the constructed system, the experimental results of 16 real-valued classification data sets show both linearly separable discrimination and random oversampling technology can help to improve the classification performance. Further, to verify its customer classification performance, we conduct an empirical analysis on 14 real-valued customer classification data sets and find that its customer classification performance is significantly better than that of the other nine models and comparable to that of the circular QCGMDH neural network model.