Multi-Label Entropy-Based Feature Selection with Applications to Insurance Purchase Prediction

Abstract

Insurance purchasing has been receiving increasing attention nowadays. Facing various insurance products, customers prefer the convincing recommendation by machine learning algorithms. The amount of personal information explosively grows due to the development of network and storage. Redundant features within the personal information may lead to reduced prediction accuracy and low efficiency. As a result, it is important to select effective features for multi-product prediction. Many current multi-label feature selection methods ignore the correlation between either features or labels. Thus, the selected features cannot comprehensively represent the leading factors of each label. In this paper, we propose a Multi-label Entropy-based Feature selection (MEF) method, aiming to provide a clear and reasonable explanation for insurance purchase prediction. Our method considers the relationship between features and removes redundant features. We compare our MEF model with 3 previously proposed multi-label feature selection methods on the Insurance Company Case datasets from CoIL Challenge 2000. The experiment results show that the MEF algorithm outperforms other methods with fewer features by 6 evaluation indexes and can effectively improve the classification performance of the multi-label classifier.