A new model for credit approval problems: A quantum-inspired neuro-evolutionary algorithm with binary-real representation

Pinho, Anderson Guimaraes de; Vellasco, Marley; Cruz, André Vargas Abs da

doi:10.1109/nabic.2009.5393327

Cited by 20 publications

(17 citation statements)

References 9 publications

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“…A quantum-inspired genetic algorithm for a credit assessment application using a neural approach is proposed by de Pinho et al (2009). Specifically, the genetic algorithm is employed to completely configure a feedforward neural network in terms of selecting the relevant input variables, the number of neurons in the hidden layer and all synaptic weights.…”

Section: Parameter Optimizationmentioning

confidence: 99%

“…On the other hand, some papers (Lacerda et al, 2005;Hoffmann et al, 2007;Martens et al, 2007;de Pinho et al, 2009;Oreski et al, 2012) include the paired t-test for assessing statistical significance of difference, but this appears to be conceptually inappropriate and statistically unsafe because parametric tests assume independence, normality, and homogeneity of variance, which are often violated due to the nature of the problems (Demsˇar, 2006).…”

Section: Statistical Significance Testsmentioning

confidence: 99%

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A literature review on the application of evolutionary computing to credit scoring

Marqués

García

Sánchez

2013

Journal of the Operational Research Society

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The last years have seen the development of many credit scoring models for assessing the creditworthiness of loan applicants. Traditional credit scoring methodology has involved the use of statistical and mathematical programming techniques such as discriminant analysis, linear and logistic regression, linear and quadratic programming, or decision trees. However, the importance of credit grant decisions for financial institutions has caused growing interest in using a variety of computational intelligence techniques. This paper concentrates on evolutionary computing, which is viewed as one of the most promising paradigms of computational intelligence. Taking into account the synergistic relationship between the communities of Economics and Computer Science, the aim of this paper is to summarize the most recent developments in the application of evolutionary algorithms to credit scoring by means of a thorough review of scientific articles published during the period 2000-2012.

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Section: Parameter Optimizationmentioning

confidence: 99%

Section: Statistical Significance Testsmentioning

confidence: 99%

A literature review on the application of evolutionary computing to credit scoring

Marqués

García

Sánchez

2013

Journal of the Operational Research Society

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“…To deal with this issue, an interesting and still littleexplored strategy in the literature related to neuroevolutionary models is the quantum-inspired evolutionary algorithms. This is a class of evolutionary algorithms developed to achieve better performance in computationally intensive problems, inspired by quantum computing principles [17,18,2,39,52,8]. One of the main advantages of the quantum-inspired evolutionary models is that good solutions are obtained with the smallest possible number of evaluations.…”

Section: Introductionmentioning

confidence: 99%

“…One of the main advantages of the quantum-inspired evolutionary models is that good solutions are obtained with the smallest possible number of evaluations. This class of algorithms has been previously used in the literature to solve combinatorial and numerical optimization problems, based on binary [18,39] and real representations [2,39,52], providing better results and using less computational effort than classical genetic algorithms [47]. Applied to neural network ensembles, quantum-inspired evolutionary algorithms can be used to model the neural networks and to determine the voting weights for each ensemble member.…”

Section: Introductionmentioning

confidence: 99%

“…-Contains a concept drift detection mechanism, with the ability to detect changes proactively or reactively. This method, already detailed in [10] allows the reaction and adjustment of the model whenever necessary; -Performs the automatic generation of new classifiers for the ensemble, most suitable for the new input data, using the quantum-inspired evolutionary algorithm for numerical and binary optimization (QIEA-BR) [39]; -Automatically determines the voting weights of each ensemble member, using the quantum-inspired evolutionary algorithm for numerical optimization (QIEA-R) [2,52], a simplified version of QIEA-BR.…”

Section: Introductionmentioning

confidence: 99%

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Neuroevolutionary learning in nonstationary environments

Escovedo

Koshiyama

Cruz³

et al. 2020

Appl Intell

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This work presents a new neuro-evolutionary model, called NEVE (Neuroevolutionary Ensemble), based on an ensemble of Multi-Layer Perceptron (MLP) neural networks for learning in nonstationary environments. NEVE makes use of quantuminspired evolutionary models to automatically configure the ensemble members and combine their output. The quantuminspired evolutionary models identify the most appropriate topology for each MLP network, select the most relevant input variables, determine the neural network weights and calculate the voting weight of each ensemble member. Four different approaches of NEVE are developed, varying the mechanism for detecting and treating concepts drifts, including proactive drift detection approaches. The proposed models were evaluated in real and artificial datasets, comparing the results obtained with other consolidated models in the literature. The results show that the accuracy of NEVE is higher in most cases and the best configurations are obtained using some mechanism for drift detection. These results reinforce that the neuroevolutionary ensemble approach is a robust choice for situations in which the datasets are subject to sudden changes in behaviour.

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