Ensemble learning with label proportions for bankruptcy prediction

Abstract

Corporate bankruptcy prediction is an interesting and important research topic that can be conceived in many practical applications. Recently, machine learning based methods have been widely proposed to solve the problem of bankruptcy prediction. However, the existing models do not consider that large amounts of instance-level labeled training data are hard to be obtained in practice. In this paper, we propose to address bankruptcy prediction problem from the perspective of learning with label proportions, where the unlabeled training data are provided in different bags and only giving the bag-level proportion of instances belonging to a particular class. Then, we contribute two novel prediction methods, termed as Bagged-pSVM and Boosted-pSVM, based on proportion support vector machines and ensemble strategies including bagging and boosting. The proposed methods can not only explicitly model the unknown instance-level labels and the known label proportions under a large-margin framework, but also improve the performance through introducing ensemble learning strategies. Extensive experiments on the benchmark datasets demonstrate their efficiency and superiority on solving the problem of bankruptcy prediction.