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Online Learning Method for Drift and Imbalance Problem in Client Credit Assessment

Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China
Author to whom correspondence should be addressed.
Symmetry 2019, 11(7), 890;
Received: 18 June 2019 / Revised: 2 July 2019 / Accepted: 4 July 2019 / Published: 8 July 2019
PDF [1979 KB, uploaded 22 July 2019]


Machine learning algorithms have been widely used in the field of client credit assessment. However, few of the algorithms have focused on and solved the problems of concept drift and class imbalance. Due to changes in the macroeconomic environment and markets, the relationship between client characteristics and credit assessment results may change over time, causing concept drift in client credit assessments. Moreover, client credit assessment data are naturally asymmetric and class imbalanced because of the screening of clients. Aiming at solving the joint research issue of concept drift and class imbalance in client credit assessments, in this paper, a novel sample-based online learning ensemble (SOLE) for client credit assessment is proposed. A novel multiple time scale ensemble classifier and a novel sample-based online class imbalance learning procedure are proposed to handle the potential concept drift and class imbalance in the client credit assessment data streams. The experiments are carried out on two real-world client credit assessment cases, which present a comprehensive comparison between the proposed SOLE and other state-of-the-art online learning algorithms. In addition, the base classifier preference and the computing resource consumption of all the comparative algorithms are tested. In general, SOLE achieves a better performance than other methods using fewer computing resources. In addition, the results of the credit scoring model and the Kolmogorov–Smirnov (KS) test also prove that SOLE has good practicality in actual client credit assessment applications. View Full-Text
Keywords: credit assessment; online learning; ensemble learning; sampling method credit assessment; online learning; ensemble learning; sampling method

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Zhang, H.; Liu, Q. Online Learning Method for Drift and Imbalance Problem in Client Credit Assessment. Symmetry 2019, 11, 890.

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