Multi-Class Learning from Label Proportions for Bank Customer Classification

Abstract

In this paper, we study multi-class learning from label proportions, and apply it to bank customer classification, in order to provide advice for banks to better manage customer relationships. We attempt to apply the multi-class extreme learning machine (ELM) to learning from label proportions (LLP). With a structure similar to neural network, ELM possesses higher computational speed and better generalization ability. As a result, it is suitable to deal with large-scale and multi-class problems. LLP is a learning problem, which classifies training data into bags where only the label proportions of each class in each bag is available. Furthermore, in order to maintain the stable model accuracy when the bag sizes increasing, we manage to add small number of labeled samples in our model, called LLP-ELM with in a semi-supervised learning framework. The experiments prove that our improvement has advantages in the case of large bag sizes. In practical, it is worth to consider to apply the proposed algorithm to multi-class learning from label proportions for bank customer classification and other multi-class scenarios.