A telecom clients’ credit risk rating model based on active learning

Abstract

The available cases with actual classes are not enough for building telecom clientspsila credit classification model in practice, especially for the newly established system in which old customerspsila data do not exist. For evaluating telecom clientspsila credit, a classifier based on active learning is proposed in this paper. Active learning aims at reducing the number of training examples to be labeled by automatically processing the unlabeled examples, then selecting the most informative ones with respect to a given cost function for a human to label. Experimental results show the model built by the active learning algorithm with less labeled training data can reach the same accuracy as passive learning. This can reduce annotation cost for credit evaluation experts.