Optimizing hyper-parameters of neural networks with swarm intelligence: a novel framework for credit scoring

66 Pages Posted: 17 Nov 2019 Last revised: 10 Feb 2023

See all articles by Runchi Zhang

Runchi Zhang

Nanjing University of Posts and Telecommunications

Zhiyi Qiu

Changzhou University

Date Written: April 11, 2020

Abstract

Neural networks are widely used in automatic credit scoring systems with high accuracy and outstanding efficiency. However, in the absence of prior knowledge, it is difficult to determine the set of hyper-parameters, which makes its application limited in practice. This paper presents a novel framework of credit-scoring model based on neural networks trained by the optimal swarm intelligence (SI) algorithm. This framework incorporates three procedures. Step 1, pre-processing, including imputation, normalization, and re-ordering of the samples. Step 2, training, where SI algorithms optimize hyper-parameters of back-propagation artificial neural networks (BP-ANN) with the area under curve (AUC) as the evaluation function. Step 3, test, applying the optimized model in Step 2 to predict new samples. The results show that the framework proposed in this paper searches the hyper-parameter space efficiently and finds the optimal set of hyper parameters with appropriate time complexity, which enhances the fitting and generalization ability of BP-ANN. Compared with existing credit-scoring models, the model in this paper predicts with a higher accuracy. Additionally, the model enjoys a greater robustness, for the difference of performance between training and testing phases.

Keywords: credit scoring, neural network, swarm intelligence algorithm

JEL Classification: G21

Suggested Citation

Zhang, Runchi and Qiu, Zhiyi, Optimizing hyper-parameters of neural networks with swarm intelligence: a novel framework for credit scoring (April 11, 2020). Available at SSRN: https://ssrn.com/abstract=3481131 or http://dx.doi.org/10.2139/ssrn.3481131

Runchi Zhang

Nanjing University of Posts and Telecommunications ( email )

Zhiyi Qiu (Contact Author)

Changzhou University ( email )

2468 Yanzheng Rd (W)
Changzhou, 213159
China

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
79
Abstract Views
413
Rank
465,361
PlumX Metrics