GMDH-based semi-supervised feature selection for customer classification

作者：

Highlights：

• We propose a semi-supervised feature selection algorithm for customer classification.

• It can utilize a few labeled samples and lots of unlabeled samples simultaneously.

• The features selected by the proposed GMDH-SSFS algorithm have a good explainability.

• The classification model trained by the selected features has good performance.

摘要

•We propose a semi-supervised feature selection algorithm for customer classification.•It can utilize a few labeled samples and lots of unlabeled samples simultaneously.•The features selected by the proposed GMDH-SSFS algorithm have a good explainability.•The classification model trained by the selected features has good performance.

论文关键词：Feature selection,Group method of data handling (GMDH),Customer classification,Semi-supervised learning

论文评审过程：Received 12 November 2016, Revised 10 June 2017, Accepted 12 June 2017, Available online 13 June 2017, Version of Record 24 July 2017.

论文官网地址：https://doi.org/10.1016/j.knosys.2017.06.018