Evolutionary Radial Basis Functions for Credit Assessment

Lacerda, E. de; Carvalho, André Carlos Ponce de Leon Ferreira; Braga, Antônio de Pádua; Ludermir, Teresa B.

doi:10.1007/s10791-005-6617-0

Cited by 25 publications

(16 citation statements)

References 51 publications

(59 reference statements)

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“…For finance applications, evolutionary clustering algorithms have been used to group either customer or company profiles, as in the works from Lacerda et al [87], for credit risk assessment, and Krovi [84], for cluster analysis of bankrupt and non-bankrupt companies.…”

Section: Applicationsmentioning

confidence: 99%

A Survey of Evolutionary Algorithms for Clustering

Hruschka

Campello

Freitas

et al. 2009

IEEE Trans. Syst., Man, Cybern. C

655

316

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-This paper presents a survey of evolutionary algorithms designed for clustering tasks. It tries to reflect the profile of this area by focusing more on those subjects that have been given more importance in the literature. In this context, most of the paper is devoted to partitional algorithms that look for hard clusterings of data, though overlapping (i.e., soft and fuzzy) approaches are also covered in the manuscript. The paper is original in what concerns two main aspects. First, it provides an up-to-date overview that is fully devoted to evolutionary algorithms for clustering, is not limited to any particular kind of evolutionary approach, and comprises advanced topics, like multi-objective and ensemble-based evolutionary clustering. Second, it provides a taxonomy that highlights some very important aspects in the context of evolutionary data clustering, namely, fixed or variable number of clusters, cluster-oriented or non-oriented operators, context-sensitive or context-insensitive operators, guided or unguided operators, binary, integer or real encodings, centroid-based, medoid-based, label-based, tree-based or graph-based representations, among others. A number of references is provided that describe applications of evolutionary algorithms for clustering in different domains, such as image processing, computer security, and bioinformatics. The paper ends by addressing some important issues and open questions that can be subject of future research.

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Section: Applicationsmentioning

confidence: 99%

A Survey of Evolutionary Algorithms for Clustering

Hruschka

Campello

Freitas

et al. 2009

IEEE Trans. Syst., Man, Cybern. C

655

316

View full text Add to dashboard Cite

show abstract

“…Since genetic algorithms are basically an optimization heuristic, Lacerda et al (2005) investigate the use of RBF neural networks designed by means of genetic algorithms and concluded that such an implementation is superior (lowest average classification error and smallest average number of hidden nodes) to other methods in a credit assessment application. Besides, while the other techniques have their parameters defined by a trial-and-error iterative process, the genetic-based approach automatically searches for the parameters of the RBF neural network.…”

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%

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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“…Evolutionary algorithms have been used for the design of RBFNs (Buchtala et al 2005;Harpham et al 2004;Lacerda et al 2001Lacerda et al , 2005 reviews can be found) but existing approaches typically suffer from the problems of a high runtime and a premature convergence in local minima. Those are both objectives to reach in any evolutionary proposal for the design of RBFNs.…”

Section: The Design Of Radial Basis Function Networkmentioning

confidence: 99%

A new hybrid methodology for cooperative-coevolutionary optimization of radial basis function networks

et al. 2006

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This paper presents a new multiobjective cooperative-coevolutive hybrid algorithm for the design of a Radial Basis Function Network (RBFN). This approach codifies a population of Radial Basis Functions (RBFs) (hidden neurons), which evolve by means of cooperation and competition to obtain a compact and accurate RBFN. To evaluate the significance of a given RBF in the whole network, three factors have been proposed: the basis function's contribution to the network's output, the error produced in the basis function radius, and the overlapping among RBFs. To achieve an RBFN composed of RBFs with proper values for these quality factors our algorithm follows a multiobjective approach in the selection process. In the design process, a Fuzzy Rule Based System (FRBS) is used to determine the possibility of applying operators to a certain RBF. As the time required by our evolutionary algorithm to converge is relatively small, it is possible to get a further improvement of the solution found by using a local minimization algorithm (for example, the Levenberg-Marquardt method). In this paper the results of applying our methodology to function approximation and time series prediction problems are also presented and compared with other alternatives proposed in the bibliography.

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Evolutionary Radial Basis Functions for Credit Assessment

Cited by 25 publications

References 51 publications

A Survey of Evolutionary Algorithms for Clustering

A Survey of Evolutionary Algorithms for Clustering

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

A new hybrid methodology for cooperative-coevolutionary optimization of radial basis function networks

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