Soft computing system for bank performance prediction

Ravi, Vadlamani; Kurniawan, Hendra; Thai, Peter Nwee Kok; Kumar, Pradeep

doi:10.1016/j.asoc.2007.02.001

Cited by 164 publications

(75 citation statements)

References 38 publications

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“…Early applications of hybrid ensembles in business failure prediction include an ensemble combining a multilayer perceptron, case-based reasoning and discriminant analyses through weighted averaging (Jo & Han, 1996) and the hybrid classifier proposed in Olmeda and Fernández (1997) consisting of a multilayer perceptron, linear discriminant analysis, logistic regression, MARS and a C4.5 decision tree. Other, more recent hybrid ensemble approaches can be found in Ravi, Kurniawan, Thai, and Kumar (2008) and Sun and Li (2008).…”

Section: Ensemble Learning For Business Failure Predictionmentioning

confidence: 99%

The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles

Bock

2017

Expert Systems with Applications

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International audienceNumerous organizations and companies rely upon business failure prediction to assess and minimize the risk of initiating business relationships with partners, clients, debtors or suppliers. Advances in research on business failure prediction have been largely dominated by algorithmic development and comparisons led by a focus on improvements in model accuracy. In this context, ensemble learning has recently emerged as a class of particularly well-performing methods, albeit often at the expense of increased model complexity. However, in practice, model choice is rarely based on predictive performance alone. Models should be comprehensible and justifiable to assess their compliance with common sense and business logic, and guarantee their acceptance throughout the organization. A promising ensemble classification algorithm that has been shown to reconcile performance and comprehensibility are rule ensembles. In this study, an extension entitled spline-rule ensembles is introduced and validated in the domain of business failure prediction. Spline-rule ensemble complement rules and linear terms found in conventional rule ensembles with smooth functions with the aim of better accommodating nonlinear simple effects of individual features on business failure. Experiments on a large selection of 21 datasets of European companies in various sectors and countries (i) demonstrate superior predictive performance of spline-rule ensembles over a set of well-established yet powerful benchmark methods, (ii) show the superiority of spline-rule ensembles over conventional rule ensembles and thus demonstrate the value of the incorporation of smoothing splines, (iii) investigate the impact of alternative term regularization procedures and (iv) illustrate the comprehensibility of the resulting models through a case study. In particular, the ability of the technique to reveal the extent and the way in which predictors impact business failure, and if and how variables interact, are exemplified

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Section: Ensemble Learning For Business Failure Predictionmentioning

confidence: 99%

The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles

Bock

2017

Expert Systems with Applications

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“…To predict the performance of business bank, some classifiers are good tools, such as K-nearest neighbors (KNN) [20], decision tree and support vector machine (SVM) [18]. In this part, we provide an overview of those three classifiers.…”

Section: Basic Classifiersmentioning

confidence: 99%

“…Jiménez et al (2007) featured the equivalent hazard rate, the growth in real gross domestic product, general economic, market and technological developments to analyze credit risk of individual bank loans [17]. Ravi et al (2007) collected 54 input variables in their dataset to predict the performance of 1000 community banks [18]. The previous mentioned macroeconomic and bank-specific determinants all can be used as features to build a classifier in this paper.…”

Section: Data Mining Of Nplmentioning

confidence: 99%

Predicting Non Performing Loan of Business Bank with Data Mining Techniques

Jie¹,

Zenglei²,

Yang³

et al. 2016

IJDTA

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“…Weighted averaging assigns weights for classifiers, and selects the class label that has the largest weighted average (Hashem, 1997;Heskes, 1997;Chan et al, 2006;Ravi et al, 2008).…”

Section: Combination Schemes In Ensembles For Classificationmentioning

confidence: 99%

“…Earlier studies on prediction modeling have focused on the building of a single best model using statistical and artificial intelligence techniques. However, since the mid1980s, integration of multiple techniques (hybrid techniques) and, by extension, combinations of the outputs of several models (ensemble techniques) have, according to the experimental results, generally outperformed individual models (Clemen, 1989;Hansen and Salamon, 1990;Leshno and Spector, 1996;Lin and McClean, 2001;Jo and Han, 1996;Olmeda and Fernandez, 1997;Shin and Han, 1998;Lin and McClean, 2001;West et al, 2005;Chan et al, 2006;Ravi et al, 2008;Sun and Li, 2008;Verikas et al, 2010). Integration has been an integral part of the effort to reinforce the ultimate performance of knowledge-based systems by improving the performance of the prediction model.…”

Section: Introductionmentioning

confidence: 99%

Impact of Ensemble Member Size on Confidence-based Selection in Bankruptcy Prediction

Kim¹,

Shin²,

Ahn³

2013

Journal of Intelligence and Information Systems

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․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․The prediction model is the main factor affecting the performance of a knowledge-based system for bankruptcy prediction. Earlier studies on prediction modeling have focused on the building of a single best model using statistical and artificial intelligence techniques. However, since the mid-1980s, integration of multiple techniques (hybrid techniques) and, by extension, combinations of the outputs of several models (ensemble techniques) have, according to the experimental results, generally outperformed individual models. An ensemble is a technique that constructs a set of multiple models, combines their outputs, and produces one final prediction. The way in which the outputs of ensemble members are combined is one of the important issues affecting prediction accuracy. A variety of combination schemes have been proposed in order to improve prediction performance in ensembles. Each combination scheme has advantages and limitations, and can be influenced by domain and circumstance. Accordingly, decisions on the most appropriate combination scheme in a given domain and contingency are very difficult. This paper proposes a confidence-based selection approach as part of an ensemble bankruptcy-prediction scheme that can measure unified confidence, even if ensemble members produce different types of continuous-valued outputs. The present experimental results show that when varying the number of models to combine, according to the creation type of ensemble members, the proposed combination method offers the best performance in the ensemble having the largest number of models, even when compared with the methods most often employed in bankruptcy prediction. ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․

show abstract

Soft computing system for bank performance prediction

Cited by 164 publications

References 38 publications

The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles

The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles

Predicting Non Performing Loan of Business Bank with Data Mining Techniques

Impact of Ensemble Member Size on Confidence-based Selection in Bankruptcy Prediction

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