Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61442
Title: A PSO-based ensemble model for peer-to-peer credit scoring
Contributor(s): Wang, Chaoqun (author); Hu, Zhongyi (author); Chiong, Raymond  (author)orcid ; Dhakal, Sandeep (author); Cheng, Xuan (author); Bao, Yukun (author)
Publication Date: 2018
DOI: 10.1109/FSKD.2018.8687154
Handle Link: https://hdl.handle.net/1959.11/61442
Abstract: 

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.

Publication Type: Conference Publication
Conference Details: ICNC-FSKD 2018: 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, Huangshan, China, 28th - 30th July, 2018
Source of Publication: ICNC-FSKD 2018 - 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, p. 412-418
Publisher: IEEE
Place of Publication: United States of America
Fields of Research (FoR) 2020: 4602 Artificial intelligence
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
Appears in Collections:Conference Publication
School of Science and Technology

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