Multi-layer Cascade Classifier for Credit Scoring with Multiple-Support Vector Machines

[Objective] This paper proposes a new multi-layer cascade classifier based on multiple-support vector machines, aiming to address the credit scoring issues of financial institutions. [Methods] The proposed hybrid model combines the ideas of genetic algorithm, machine learning and ensemble learning. The framework includes support vector machine classifier, normalization method, feature extraction, parameter optimization, 10-fold cross evaluation and other technologies. We tested the layer deepening strategy, attribute reuse method, and fitness function diversification by experiment. [Results] We examined the support vector machine optimized by genetic algorithm with Australian Credit Approval dataset. The prediction accuracy was improved as the increase of layers, and the overall frame prediction accuracy reached 93.33%. [Limitations] The proposed method only uses SVM, which needs to be expanded. There are many classifiers in the framework, which took long time to train and optimize. [Conclusions] The proposed classifier could effectively improve credit scoring services, and finish similar binary classification tasks. © 2021 The Author(s).