A PSO-based ensemble model for peer-to-peer credit scoring

Title: A PSO-based ensemble model for peer-to-peer credit scoring
Creator: Wang, Chaoqun; Chiong, Raymond; Chen, Yuan; Hu, Zhongyi; Dhakal, Sandeep; Bao, Yukun
Relation: 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). Proceedings of 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) (Huangshan, China 28-30 July, 2018) p. 412-418
Publisher Link: http://dx.doi.org/10.1109/FSKD.2018.8687154
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Resource Type: conference paper
Date: 2018
Description: We propose a multi-classifier ensemble model based on particle swarm optimization (PSO) for the evaluation of personal credit risk in peer-to-peer (P2P) lending platforms. In the proposed method, we consider the differences and complementarity of the base classifiers' performance and use PSO to optimize their weights. Experimental results show that our proposed P2P personal credit scoring model outperforms both single and other benchmark ensemble models. Among the examined model variants, the ensemble model based on PSO with 100 particles is the best.
Subject: P2P online lending; credit scoring; ensemble models; particle swarm optimization
Identifier: http://hdl.handle.net/1959.13/1446690
Identifier: uon:42943
Identifier: ISBN:9781538680971
Language: eng
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