Evolutionary optimization of transition probability matrices for credit decision-making

Zhang, Jingqiao; Avasarala, Viswanath; Subbu, Raj

doi:10.1016/j.ejor.2009.01.020

Cited by 35 publications

(11 citation statements)

References 9 publications

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“…Credit rating analysis concentrates on the former subjects and assesses their capability and willingness to meet payable obligations. A common application of predictive modeling is to mimic the evaluation process of rating agencies such as Moody's (e.g., Hájek, 2011), and, more specifically, to estimate the transition probability of an entity with given rating being moved to a higher/lower risk category (e.g., Kim & Sohn, 2008;Zhang et al, 2010b). Corporate risk models are also developed by financial institutions to inform lending decisions and to predict bankruptcy in particular (e.g, Fethi & Pasiouras, 2010;Kumar & Ravi, 2007).…”

Section: Related Workmentioning

confidence: 99%

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Data mining techniques: Modern approaches to application in credit scoring

Volkova¹,

Гисин²,

Solov'ev³

2017

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state-of-the-art in credit scoring. In addition, we repeat our benchmark for several performance measures and examine the degree to which these differ in their assessment of candidate scorecards. Our study provides valuable insight for professionals and academics in credit scoring.The paper helps practitioners to stay abreast of technical advancements in predictive modeling.From an academic standpoint, the study provides an independent assessment of recent scoring methods and offers a new baseline to which future approaches can be compared.

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Section: Related Workmentioning

confidence: 99%

“…Typically, the term credit scoring refers to credit decisions in the retail business. The management of corporate or national credit is considered in the literature on business failure prediction or credit rating analysis, respectively (e.g., Kumar & Ravi, 2007;Zhang et al, 2010b).…”

Section: Introductionmentioning

confidence: 99%

Data mining techniques: Modern approaches to application in credit scoring

Volkova¹,

Гисин²,

Solov'ev³

2017

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“…JADE has shown its success for a variety of benchmark problems of different characteristics [2] [14] and in different real world applications including credit decision making [18], air traffic management [19], and combinatorial auction [20].…”

Section: B Jade: An Adaptive Differential Evolution Algorithmmentioning

confidence: 99%

An adaptive coevolutionary Differential Evolution algorithm for large-scale optimization

Yang

Zhang

Tang

et al. 2009

2009 IEEE Congress on Evolutionary Computation

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Abstract-In this paper, we propose a new algorithm, named JACC-G, for large scale optimization problems. The motivation is to improve our previous work on grouping and adaptive weighting based cooperative coevolution algorithm, DECC-G [1], which uses random grouping strategy to divide the objective vector into subcomponents, and solve each of them in a cyclical fashion. The adaptive weighting mechanism is used to adjust all the subcomponents together at the end of each cycle. In the new JACC-G algorithm: (1) A most recent and efficient Differential Evolution (DE) variant, JADE [2], is employed as the subcomponent optimizer to seek for a better performance; (2) The adaptive weighting is time-consuming and expected to work only in the first few cycles, so a detection module is added to prevent applying it arbitrarily; (3) JADE is also used to optimize the weight vector in adaptive weighting process instead of using a basic DE in previous DECC-G. The efficacy of the proposed JACC-G algorithm is evaluated on two sets of widely used benchmark functions up to 1000 dimensions.

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“…It has been proved that DE is an accurate, reasonably fast, and robust optimizer for many optimization problems in real-world applications such as filter design, PID control, image segmentation, and other scientific and engineering problems [6][7][8][9][10][11][12][13][14][15]. The DE has a similar framework with Genetic Algorithm but only a few control variables.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Binary Differential Evolution with Parameter Adaptation

Jia¹,

Duan²,

Khan³

2013

IJCIS

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Differential Evolution (DE) has been applied to many scientific and engineering problems for its simplicity and efficiency. However, the standard DE cannot be used in a binary search space directly. This paper proposes an adaptive binary Differential Evolution algorithm, or ABDE, that has a similar framework as the standard DE but with an improved binary mutation strategy in which the best individual participates. To further enhance the search ability, the parameters of the ABDE are slightly disturbed in an adaptive manner. Experiments have been carried out by comparing ABDE with two binary DE variants, normDE and BDE, and the most used binary search technique, GA, on a set of 13 selected benchmark functions and the classical 0-1 knapsack problem. Results show that the ABDE performs better than, or at least comparable to, the other algorithms in terms of search ability, convergence speed, and solution accuracy.

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Evolutionary optimization of transition probability matrices for credit decision-making

Cited by 35 publications

References 9 publications

Data mining techniques: Modern approaches to application in credit scoring

Data mining techniques: Modern approaches to application in credit scoring

An adaptive coevolutionary Differential Evolution algorithm for large-scale optimization

An Efficient Binary Differential Evolution with Parameter Adaptation

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