GMDH-based semi-supervised feature selection for customer classification

Abstract

Data dimension reduction is an important step for customer classification modeling, and feature selection has been a research focus of the data dimension reduction field. This study introduces the group method of data handling (GMDH), puts forward a GMDH-based semi-supervised feature selection (GMDH-SSFS) algorithm, and applies it to customer feature selection. The algorithm can utilize a few samples with class labels L, and a large number of samples without class labels U simultaneously. What is more, it considers the relationship between features and class labels, and that between features during feature selection. The GMDH-SSFS model mainly consists of three stages: 1) Train N basic classification models based on the dataset L with class labels; 2) Label samples selectively in the dataset U without class labels, and add them to L; 3) Train the GMDH neural network based on the new training set L, and select the optimal feature subset FS. Based on an empirical analysis of four customer classification datasets, results suggest that the features selected by the GMDH-SSFS model have a good explainability. Meanwhile, the customer classification performance of the classification model trained by the selected feature subset is superior to that of the models trained by the commonly used Laplacian score (an unsupervised feature selection algorithm), Fisher score (a supervised feature selection algorithm), and the FW-SemiFS and S3VM-FS (two semi-supervised feature selection algorithms).