A comparative assessment of ensemble learning for credit scoring

Wang, Gang; Jiang, Hao; Ma, Jian; Jiang, Hongbing

doi:10.1016/j.eswa.2010.06.048

Cited by 441 publications

(266 citation statements)

References 22 publications

(31 reference statements)

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“…linear discriminant analysis, linear regression, logit, probit, tobit and binary tree) "are reported to have a lack of accuracy [in this field]". In addition, the current approaches for residential real estate risk evaluation are usually limited by: (1) lack of necessary data (Lopez, Saidenberg 2000); (2) lack of rationality in the way trade-offs between criteria are calculated (Ferreira et al 2012); and (3) the need to make subjectivity explicit in the decision making process (Santos et al 2002) (for further discussion, see also Wang et al 2011).…”

Section: Risk Assessment Of Real Estate Investments and Related Workmentioning

confidence: 99%

A Fuzzy Knowledge-Based Framework for Risk Assessment of Residential Real Estate Investments

Ribeiro¹,

Ferreira

Jalali

et al. 2017

Technological and Economic Development of Economy

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Abstract. Risk analysis of residential real estate investments requires careful analysis of certain variables (or determinants). Because real estate is a key sector for economic and social development, this risk analysis is seen as critical in supporting decision processes relating to buying or selling residential properties, partly due to the pressures caused by the current economic environment. This study aims to develop a conceptual reference model for risk assessment of residential real estate using fuzzy cognitive mapping. This fuzzy model allows cause-and-effect relationships between determinants to be identified and better understood, which in turn allows for better informed investment decisions. The results show that the use of cognitive maps reduces the number of omitted criteria and favors learning with regard to how the criteria relate to each other, holding great potential and versatility in structuring complex decision problems. Practical implications, strengths and weaknesses of our proposal are discussed.

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Section: Risk Assessment Of Real Estate Investments and Related Workmentioning

confidence: 99%

A Fuzzy Knowledge-Based Framework for Risk Assessment of Residential Real Estate Investments

Ribeiro¹,

Ferreira

Jalali

et al. 2017

Technological and Economic Development of Economy

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“…In order to evaluate the performance of proposed model, other method for credit scoring is employed. The result of [27] for comparing common and popular methods of ensemblebagging, boosting and stacking-with the result of model, is also presented. It is obvious that the proposed hybrid ensemble model has better performance.…”

Section: Fusion Agentmentioning

confidence: 99%

“…In contrast to ordinary machine learning approaches that try to learn one hypothesis from training data, ensemble methods try to construct a set of hypotheses and combine them to use [27]. This method is used to improve the performance and accuracy of classification task.…”

Section: Overview Of Ensemble Learningmentioning

confidence: 99%

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A Hybrid Support Vector Machine Ensemble Model for Credit Scoring

Ghodselahi¹

2011

IJCA

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Credit risk is the most challenging risk to which financial institution are exposed. Credit scoring is the main analytical technique for credit risk assessment. In this paper a hybrid model for credit scoring is designed which applies ensemble learning for credit granting decisions. The hybrid model combines clustering and classification techniques. Ten Support Vector Machine (SVM) classifiers are utilized as the members of ensemble model. Since even a small improvement in credit scoring accuracy causes significant loss reduction, then the application of ensemble in hybrid model leads to better performance of classification. A real dataset is used to test the model performance. The test results shows that proposed hybrid SVM ensemble has better classification accuracy when compared with other methods.

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“…From the many comparative studies carried out (Baesens et al 2003, Huang et al 2004, Xiao et al 2006, Wang et al 2011, it is not possible to claim the superiority of a method over other competing algorithms regardless of data characteristics. For instance, noisy samples, missing values, skewed class distribution and attribute relevance may significantly affect the success of most prediction models.…”

Section: Introductionmentioning

confidence: 99%

On the suitability of resampling techniques for the class imbalance problem in credit scoring

Marqués

García

Sánchez

2013

Journal of the Operational Research Society

127

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In real-life credit scoring applications, the case in which the class of defaulters is under-represented in comparison to the class of non-defaulters is a very common situation, but it has still received little attention. The present paper investigates the suitability and performance of several resampling techniques when applied in conjunction with statistical and artificial intelligence prediction models over five real-world credit data sets, which have artificially been modified to derive different imbalance ratios (proportion of defaulters and non-defaulters examples). Experimental results demonstrate that the use of resampling methods consistently improves the performance given by the original imbalanced data. Besides, it is also important to note that in general, over-sampling techniques perform better than any under-sampling approach.

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A comparative assessment of ensemble learning for credit scoring

Cited by 441 publications

References 22 publications

A Fuzzy Knowledge-Based Framework for Risk Assessment of Residential Real Estate Investments

A Fuzzy Knowledge-Based Framework for Risk Assessment of Residential Real Estate Investments

A Hybrid Support Vector Machine Ensemble Model for Credit Scoring

On the suitability of resampling techniques for the class imbalance problem in credit scoring

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