Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem

Sexton, Randall S.; McMurtrey, Shannon; Cleavenger, Dean

doi:10.1016/j.ejor.2004.05.018

Cited by 30 publications

(17 citation statements)

References 22 publications

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“…A hybrid model of neural network and adaptive boosting method was studied by Xu et al (2015). Sexton and Mcmurtrey (2006) and Setiono et al (2009) studied a genetic algorithm-based neural network algorithm for credit card screening and found that the neural network rule extraction is very effective in discovering knowledge and is particularly appropriate in applications that require comprehensibility and accuracy. A two-stage hybrid model based on artificial neural networks and multivariate adaptive regression splines (MARs) was proposed for credit scoring by Lee and Chen (2005).…”

Section: Financial Crisis and Risk Modelingmentioning

confidence: 99%

Decision analytics and machine learning in economic and financial systems

Qiao

Beling

2016

Environ Syst Decis

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Decision analytics may be viewed as the combined use of predictive modeling techniques (e.g., forecasting and machine learning) and prescriptive decision frameworks (e.g., optimization and simulation) to create value in systems. Recent years have seen an explosion in decision analytics applications, driven by advances in machine learning methods and computational optimization and by massive increases in the data to which these techniques may be applied. Decision analytics has long been used in the domains of economic and financial systems, with credit scoring being an example of an early success, and the clear trend is to the development of ever more sophisticated methods and applications.This issue of Environment System & Decisions is devoted to decision analytics in financial and economic systems and environments. The papers in the issue each bear an influence from theoretical developments and financial and economic applications of statistical methods, machine learning, and decision and risk modeling techniques. Before introducing the papers, we review some of the influential literature in this regard.

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Section: Financial Crisis and Risk Modelingmentioning

confidence: 99%

Decision analytics and machine learning in economic and financial systems

Qiao

Beling

2016

Environ Syst Decis

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“…To overcome this limitation, new information generated from an ANN should be converted into more comprehensible representations; these processes of knowledge discovery from analysis tools are referred to as rule extraction techniques (Núñez et al 2002). The comprehensibility of ANN systems can be improved by generating rules from the trained model This study presents a discussion on the knowledge discovery using the neural network simultaneous optimization algorithm (NNSOA) (Sexton et al 2006), recursive rule extraction (Re-RX) based on neural networks (Setiono et al 2009), and decision tree learners to learn what the SVM has learned (Martens et al 2007) using the same credit card screening data set. First, the NNSOA specific to credit card screening is a genetic algorithm (GA)-based neural network training algorithm.…”

Section: Introductionmentioning

confidence: 99%

Rule extraction algorithm from support vector machines and its application to credit screening

Chen

2011

Soft Comput

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Developing rule extraction algorithms from machine learning techniques such as artificial neural networks and support vector machines (SVMs), which are considered incomprehensible black-box models, is an important topic in current research. This study proposes a rule extraction algorithm from SVMs that uses a kernelbased clustering algorithm to integrate all support vectors and genetic algorithms into extracted rule sets. This study uses measurements of accuracy, sensitivity, specificity, coverage, fidelity and comprehensibility to evaluate the performance of the proposed method on the public credit screening data sets. Results indicate that the proposed method performs better than other rule extraction algorithms. Thus, the proposed algorithm is an essential analysis tool that can be effectively used in data mining fields.

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“…In the present review, Sexton et al (2006), Garcı´a et al (2012) and Vukovic et al (2012) report a Wilcoxon signed rank test to statistically compare the performance of the algorithms, whereas Finlay (2010) employs a Kruskal-Wallis test to show that differences between pairs of techniques are significant.…”

Section: Statistical Significance Testsmentioning

confidence: 99%

“…Finally, it should be noted that only a small percentage of the studies utilize some measure of efficiency, such as CPU time (Chen and Huang, 2003;Sexton et al, 2006;Liu et al, 2008;Zhou et al, 2009;Correa and Gonza´lez, 2011;Hens and Tiwari, 2012), number of rules generated (Hoffmann et al, 2002(Hoffmann et al, , 2007Martens et al, 2007;Lahsasna et al, 2008;Ainon et al, 2009) and decision tree size (Mahmoud et al, 2010). These two latter metrics, the number of rules and the decision tree size, can also be viewed as a way to measure the comprehensibility or transparency of the model, which has become a very important factor for realistic credit scoring systems.…”

Section: Performance Evaluation Criteriamentioning

confidence: 99%

“…These characteristics are then used in a linear discriminant analysis credit scoring model, concluding that this may reduce the risk of credit prediction. Sexton et al (2006) apply a genetic-based algorithm, called the neural network simultaneous optimization algorithm, to a credit approval data set. The algorithm determines those input variables that contribute to estimation of the underlying function.…”

Section: Variable Selectionmentioning

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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Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem

Cited by 30 publications

References 22 publications

Decision analytics and machine learning in economic and financial systems

Decision analytics and machine learning in economic and financial systems

Rule extraction algorithm from support vector machines and its application to credit screening

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

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