Building credit scoring models using genetic programming

Ong, Chorng‐Shyong; Huang, Jih-Jeng; Tzeng, Gwo‐Hshiung

doi:10.1016/j.eswa.2005.01.003

Cited by 292 publications

(142 citation statements)

References 30 publications

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“…Overall, the SVM linear kernel performs fractionally better than logistic regression followed closely by the SVM with RBF kernel. However it should be noted that this is a generalised logistic regression model and financial institutions typically have at their disposal methods to increase and extend its accuracy and flexibility [30]. The Naïve Bayes performs worst of the two-class classifiers.…”

Section: Methodsmentioning

confidence: 99%

Learning without Default: A Study of One-Class Classification and the Low-Default Portfolio Problem

Kennedy¹,

Namee²,

Delany³

2010

Artificial Intelligence and Cognitive Science

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Abstract. This paper asks at what level of class imbalance one-class classifiers outperform two-class classifiers in credit scoring problems in which class imbalance, referred to as the low-default portfolio problem, is a serious issue. The question is answered by comparing the performance of a variety of one-class and two-class classifiers on a selection of credit scoring datasets as the class imbalance is manipulated. We also include random oversampling as this is one of the most common approaches to addressing class imbalance. This study analyses the suitability and performance of recognised two-class classifiers and one-class classifiers. Based on our study we conclude that the performance of the twoclass classifiers deteriorates proportionally to the level of class imbalance. The two-class classifiers outperform one-class classifiers with class imbalance levels down as far as 15% (i.e. the imbalance ratio of minority class to majority class is 15:85). The one-class classifiers, whose performance remains unvaried throughout, are preferred when the minority class constitutes approximately 2% or less of the data. Between an imbalance of 2% to 15% the results are not as conclusive. These results show that one-class classifiers could potentially be used as a solution to the low-default portfolio problem experienced in the credit scoring domain.

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Section: Methodsmentioning

confidence: 99%

Learning without Default: A Study of One-Class Classification and the Low-Default Portfolio Problem

Kennedy¹,

Namee²,

Delany³

2010

Artificial Intelligence and Cognitive Science

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“…The second model is ANN, which is known for its excellent ability of learning non-linear relationships in a system. The third model is rough sets, which is one kind of induction based algorithms, and has been widely used in classification problems since 1990s [2].Logistic regression model is one of the most popular statistical tools for classification problems and is more suitable for the credit scoring problems. [2]Artificial neural networks were developed to mimic the neurophysiology of the human brain to be a type of flexible non-linear regression, discriminant, and clustering models [2] Rough sets is a mathematical tool used to deal with vagueness or uncertainty Compared to fuzzy sets [2] B.…”

Section: A Data Mining and Credit Scoringmentioning

confidence: 99%

“…The third model is rough sets, which is one kind of induction based algorithms, and has been widely used in classification problems since 1990s [2].Logistic regression model is one of the most popular statistical tools for classification problems and is more suitable for the credit scoring problems. [2]Artificial neural networks were developed to mimic the neurophysiology of the human brain to be a type of flexible non-linear regression, discriminant, and clustering models [2] Rough sets is a mathematical tool used to deal with vagueness or uncertainty Compared to fuzzy sets [2] B. Fuzzy EXPERT System A fuzzy expert system is simply an expert system that uses a collection of fuzzy membership functions and rules, instead of Boolean logic, to reason about data The fuzzy Inference Systems (FIS) are very good tools as they hold the nonlinear universal approximation.…”

Section: A Data Mining and Credit Scoringmentioning

confidence: 99%

“…Genetic programming (GP) is used in a survey to build credit scoring models. It was concluded that GP can provide better performance than other models [2]. Fuzzy Adaptive Network (FAN) has been used in another paper to model the credit rating of small financial enterprises.…”

Section: Introductionmentioning

confidence: 99%

“…Two of the most commonly used statistical techniques are Linear Discriminant Analysis and Logistic Regression [1]. These techniques leads to an uncertainty described as the classification problem which should be improved [2]. Credit scoring models have been widely used by financial institutions to determine if loan customers belong to either a good applicant group or a bad applicant group .The advantages of using credit scoring models can be described as the benefit from reducing the cost of credit analysis, enabling faster credit decision, insuring credit collections, and diminishing possible risk [3,4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Approach for Labeling the Class of Bank Credit Customers via Classification Method in Data Mining

Nosratabadi¹,

Pourdarab²,

Nadali³

2011

IJIET

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Abstract-ONE of the most important parts in credit scoring is determining the class of customers to run the Data Mining algorithms. The purpose of this research is estimating the Label of Credit customers via Fuzzy Expert System. Here the class of customers has been specified by a Fuzzy Expert System and then the Data Mining Algorithms have been run on the final data with Clementine software. The presented steps have been studied in an Iranian Bank as empirical study.

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Improving Forecast of Binary Rare Events Data: A GAM‐Based Approach

Calabrese

Osmetti

2015

Journal of Forecasting

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This paper develops a method for modelling binary response data in a regression model with highly unbalanced class sizes. When the class sizes are highly unbalanced and the minority class represents a rare event, conventional regression analysis, i.e. logistic regression models, could underestimate the probability of the rare event. To overcome this drawback, we introduce a flexible skewed link function based on the quantile function of the generalized extreme value (GEV) distribution in a generalized additive model (GAM). The proposed model is known as generalized extreme value additive (GEVA) regression model, and a modified version of the local scoring algorithm is suggested to estimate it. We apply the proposed model to a dataset on Italian small and medium enterprises (SMEs) to estimate the default probability of SMEs. Our proposal performs better than the logistic (linear or additive) model in terms of predictive accuracy. Copyright © 2015 John Wiley & Sons, Ltd.

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Building credit scoring models using genetic programming

Cited by 292 publications

References 30 publications

Learning without Default: A Study of One-Class Classification and the Low-Default Portfolio Problem

Learning without Default: A Study of One-Class Classification and the Low-Default Portfolio Problem

A New Approach for Labeling the Class of Bank Credit Customers via Classification Method in Data Mining

Improving Forecast of Binary Rare Events Data: A GAM‐Based Approach

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