Comparison of the Hybrid Credit Scoring Models Based on Various Classifiers

Feilong Chen · Feng-Chia Li

22 citations0/0 files storedInternational Journal of Intelligent Information Technologies

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Financial Distress and Bankruptcy Prediction Imbalanced Data Classification Techniques Machine Learning and ELM

Abstract

Credit scoring is an important topic for businesses and socio-economic establishments collecting huge amounts of data, with the intention of making the wrong decision obsolete. In this paper, the authors propose four approaches that combine four well-known classifiers, such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Back-Propagation Network (BPN) and Extreme Learning Machine (ELM). These classifiers are used to find a suitable hybrid classifier combination featuring selection that retains sufficient information for classification purposes. In this regard, different credit scoring combinations are constructed by selecting features with four approaches and classifiers than would otherwise be chosen. Two credit data sets from the University of California, Irvine (UCI), are chosen to evaluate the accuracy of the various hybrid features selection models. In this paper, the procedures that are part of the proposed approaches are described and then evaluated for their performances.

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2000Neural network credit scoring models 2003Benchmarking state-of-the-art classification algorithms for credit scoring 2006Credit scoring with a data mining approach based on support vector machines 2000A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers 1996A comparison of neural networks and linear scoring models in the credit union environment 2005A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines 2005Building credit scoring models using genetic programming 2002Credit scoring using the hybrid neural discriminant technique

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