Understanding Bank Failure: A Close Examination of Rules Created by Genetic Programming

García-Almanza, Alma Lilia; Alexandrova-Kabadjova, Biliana; Martínez-Jaramillo, Serafín

doi:10.1109/cerma.2010.14

Cited by 6 publications

(1 citation statement)

References 8 publications

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“…Tokinaga et al [16] used GP to extract rules from a neural network to provide explanatory classifications of data. Other work used GP to generate a set of comprehensible decision rules to identify cases of bankruptcy [10]. Decision-trees have been used for classification in a wide range of applications, with recent proposals for developing meta decision trees for explainable recommendation systems [14].…”

Section: Previous Workmentioning

confidence: 99%

Automated, Explainable Rule Extraction from MAP-Elites Archives

Urquhart

Höhl

Hart

2021

Applications of Evolutionary Computation

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Quality-diversity (QD) algorithms that return a large archive of elite solutions to a problem provide insights into how high-performing solutions are distributed throughout a feature-space defined by a user -they are often described as illuminating the feature-space, providing a qualitative illustration of relationships between features and objective quality. However, if there are 1000s of solutions in an archive, extracting a succinct set of rules that capture these relationships in a quantitative manner (i.e. as a set of rules) is challenging. We propose two methods for the automated generation of rules from data contained in an archive; the first uses Genetic Programming and the second, a rule-induction method known as CN2. Rules are generated from large archives of data produced by running MAP-Elites on an urban logistics problem. A quantitative and qualitative evaluation that includes the end-user demonstrate that the rules are capable of fitting the data, but also highlights some mismatches between the model used by the optimiser and that assumed by the user.

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Section: Previous Workmentioning

confidence: 99%

Automated, Explainable Rule Extraction from MAP-Elites Archives

Urquhart

Höhl

Hart

2021

Applications of Evolutionary Computation

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Bankruptcy Prediction for Banks: An Artificial Intelligence Approach to Improve Understandability

García-Almanza

Alexandrova-Kabadjova

Martínez-Jaramillo

2013

Studies in Computational Intelligence

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Evolutionary feature selection approaches for insolvency business prediction with genetic programming

2023

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This study uses different feature selection methods in the field of business failure prediction and tests the capability of Genetic Programming (GP) as an appropriate classifier in this field. The prediction models categorize the insolvency/non-insolvency of a firm one year in advance from a large set of financial ratios. Different selection strategies based on two evolutionary algorithms were used to reduce the dimensionality of the financial features considered. The first method considers the combination between the global search provided by an evolutionary algorithm (differential evolution) with a simple classifier, together with the possible use of classical filters in a first step of feature selection. Secondly, genetic programming is used as a feature selector. In addition, these selection approaches will be compared when GP is used exclusively as a classifier. The results show that, when using GP as a classifier method, the proposed selection method with GP stands out from the rest. Moreover, the use of GP as a classifier improves the results with respect to other classifier methods. This shows an added value to the use of GP in this field, in addition to the interpretability of GP prediction models.

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Understanding Bank Failure: A Close Examination of Rules Created by Genetic Programming

Cited by 6 publications

References 8 publications

Automated, Explainable Rule Extraction from MAP-Elites Archives

Automated, Explainable Rule Extraction from MAP-Elites Archives

Bankruptcy Prediction for Banks: An Artificial Intelligence Approach to Improve Understandability

Evolutionary feature selection approaches for insolvency business prediction with genetic programming

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