Filomat 2016 Volume 30, Issue 15, Pages: 4027-4036
https://doi.org/10.2298/FIL1615027Y
Full text ( 308 KB)
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An elm-based classification algorithm with optimal cutoff selection for credit risk assessment
Yu Lean (Beijing University of Chemical Technology, School of Economics and Management, Beijing, China)
Li Xinxie (Beijing University of Chemical Technology, School of Economics and Management, Beijing, China)
Tang Ling (Beihang University, School of Economics and Management, Beijing, China)
Gao Li (Beijing University of Posts and Telecommunications, Beijing, China)
In this paper, an extreme learning machine (ELM) classification algorithm
with optimal cutoff selection is proposed for credit risk assessment.
Different from existing models using a fixed cutoff value (0.0 or 0.5), the
proposed classification model especially considers the optimal cutoff value
as one important evaluation parameter in credit risk modeling, to enhance
the assessment accuracy. In particular, using the powerful artificial
intelligence (AI) tool of ELM as the basic classification, the simple but
efficient optimization algorithm of grid search is employed to select the
optimal cutoff value. Accordingly, three main steps are included: (1) ELM
training using the training dataset, (2) cutoff optimization via the grid
search method using the training and validation datasets, and (3)
classification generalization based on the trained ELM and optimal cutoff
using the testing dataset. For illustration and verification, the
experimental study with two publicly available credit datasets as the study
samples confirms the superiority of the proposed ELM-based classification
algorithm with optimal cutoff selection over other some popular
classification techniques without cutoff selection.
Keywords: credit risk assessment, cutoff selection, extreme learning machine, grid search method