A Novel Prediction Method Based on Improved Binary Glowworm Swarm Optimization and Multi-Fractal Dimension for P2P Lending Investment Risk

The frequent bankruptcy incidents of peer-to-peer (P2P) lending industry have damaged the benefits of investors in China, and how to accurately and efficiently predict the investment risks of P2P lending becomes an important problem. For this very reason, a novel prediction method based on improved binary glowworm swarm optimization and multi-fractal dimension (IBGSOMFD) for P2P lending investment risk is proposed. Firstly, we propose an improved binary glowworm swarm optimization, abbreviated IBGSO, by uniformly designing an initial population using the good-point set theory, improving the moving way of glowworms, and introducing the mechanism of population diffusion and variation. Secondly, IBGSO combined with multi-fractal dimension (MFD) is applied to feature selection, and the optimal subset extracted from the original dataset can be efficiently achieved utilizing IBGSO, which can reduce its redundant attributes, and retain its pivotal attributes of P2P lending investment risk. Finally, an investment risk prediction model of P2P lending based on support vector machine (SVM) is established, which can accurately and efficiently predict the investment risk of P2P lending. Experimental results on 6 University of California Irvine (UCI) benchmark datasets show that IBGSOMFD outperforms other state-of-the-art approaches in predictive ability and calculative efficiency, and its effectiveness and significance. After the performance verification of IBGSOMFD, this work looks at how it can be applied in the risk prediction of P2P lending investment in China to maintain a stable market order.

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