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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...
It is difficult to get satisfactory customer churn prediction effect for the traditional model, because the class distribution of customer data is often imbalanced, and the available data in target task is little. This paper combines the transfer learning with the ensemble learning, and proposes a feature selection based transfer ensemble model (FSTE). It utilizes the customer data in both the related...
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