A credit risk evaluation model for telecom clients based on query-by-committee method of active learning

Evaluating telecom clients' credit risk rate is classifying their credit risk level. An approach based on active learning was proposed for solving the insufficient labeled data problem in building a credit risk rate classifier. The new QBC (query-by-committee, QBC) method of active learning was presented to improve the classifier's accuracy. By applying the actual telecom clients data in the experiment, the results show that the model built by the new algorithm with less labeled training data can reach the same accuracy as passive learning. This can reduce annotation cost for credit evaluation experts.