Credit scoring using three-way decisions with probabilistic rough sets

Maldonado, Sebastián; Peters, Georg; Weber, Richard W.

doi:10.1016/j.ins.2018.08.001

Cited by 78 publications

(21 citation statements)

References 29 publications

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“…In future work, we will improve the decision-making accuracy of grey fuzzy variables and apply it to high-precision medical and military fields. In terms of theoretical research, we hope to choose a more complex form to express fuzzy parts, such as Gaussian fuzzy number [44,45], intuitionistic fuzzy sets [46,47], and rough sets [48]. At the same time, BPM operators can be simplified to improve computational efficiency.…”

Section: Discussionmentioning

confidence: 99%

Grey Fuzzy Multiple Attribute Group Decision-Making Methods Based on Interval Grey Triangular Fuzzy Numbers Partitioned Bonferroni Mean

2020

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Considering the characteristics such as fuzziness and greyness in real decision-making, the interval grey triangular fuzzy number is easy to express fuzzy and grey information simultaneously. And the partition Bonferroni mean (PBM) operator has the ability to calculate the interrelationship among the attributes. In this study, we combine the PBM operator into the interval grey triangular fuzzy numbers to increase the applicable scope of PBM operators. First of all, we introduced the definition, properties, expectation, and distance of the interval grey triangular fuzzy numbers, and then we proposed the interval grey triangular fuzzy numbers partitioned Bonferroni mean (IGTFPBM) and the interval grey triangular fuzzy numbers weighted partitioned Bonferroni mean (IGTFWPBM), the adjusting of parameters in the operator can bring symmetry effect to the evaluation results. After that, a novel method based on IGTFWPBM is developed for solving the grey fuzzy multiple attribute group decision-making (GFMAGDM) problems. Finally, we give an example to expound the practicability and superiority of this method.

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Section: Discussionmentioning

confidence: 99%

Grey Fuzzy Multiple Attribute Group Decision-Making Methods Based on Interval Grey Triangular Fuzzy Numbers Partitioned Bonferroni Mean

2020

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“…In this context, the development of the Z-Score was proposed in the year 1968 by Altman [32] that has been applied in many companies in the financial sector. For the application of credit scoring, in recent years, several new techniques have appeared, namely: Decision Trees [33], Artificial Neural Networks [12], Support Vector Machines [9], Rough Sets [19], Deep Learning [15], and Metaheuristic algorithms [34], among others.…”

Section: Computational Intelligence Models For Financial Applicationsmentioning

confidence: 99%

“…Regarding the topic of our research, it is possible to find research papers where attempts to solve the problem of credit scoring are reported. Various supervised classification models have been used in these investigations; the use of Support Vector Machines [7][8][9], Artificial Neural Networks [10][11][12] and Classifier Ensembles [13][14][15][16], among others [17][18][19], stands out. Some of the experimental comparisons made to determine the performance of the classifiers in terms of credit assignment [20][21][22][23] exhibit, in our opinion, certain problems that prevent generalizing the published results.…”

Section: Introductionmentioning

confidence: 99%

Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers

Rangel-Díaz-de-la-Vega

Villuendas-Rey

Yáñez-Márquez

et al. 2020

Applied Sciences

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In this paper, an experimental study was carried out to determine the influence of imbalanced datasets preprocessing in the performance of associative classifiers, in order to find the better computational solutions to the problem of credit scoring. To do this, six undersampling algorithms, six oversampling algorithms and four hybrid algorithms were evaluated in 13 imbalanced datasets referring to credit scoring. Then, the performance of four associative classifiers was analyzed. The experiments carried out allowed us to determine which sampling algorithms had the best results, as well as their impact on the associative classifiers evaluated. Accordingly, we determine that the Hybrid Associative Classifier with Translation, the Extended Gamma Associative Classifier and the Naïve Associative Classifier do not improve their performance by using sampling algorithms for credit data balancing. On the other hand, the Smallest Normalized Difference Associative Memory classifier was beneficiated by using oversampling and hybrid algorithms.

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“…Traditionally, the logistic model was often used to predict defaults and today is still useful for benchmarking thanks to its simplicity, interpretability and dependability [34,4,14]. However, more sophisticated and innovative approaches are also used, like neural networks [34,38,15], smart ubiquitous data mining [4], theory of three-way decisions [20], and theory of survival [14]. In our paper, we introduce a novel approach that starts with creating a large number of features (over 6,000 here) and then reducing them to a few well performing subsets.…”

Section: Credit Risk Managementmentioning

confidence: 99%

Predicting complex user behavior from CDR based social networks

Doyle

Herga

Dipple

et al. 2019

Information Sciences

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Call Detail Record (CDR) datasets provide enough information about personal interactions of cell phone service customers to enable building detailed social networks. We take one such dataset and create a realistic social network to predict which customer will default on payments for the phone services, a complex behavior combining social, economic, and legal considerations. After extracting a large feature set from this network, we find that each feature poorly correlates with the default status. Hence, we develop a sophisticated model to enable reliable predictions. Our main contribution is a methodology for building complex behavior models from very large sets of diverse features and using different methods to choose those features that perform best for the final model. This approach enables us to identify the most efficient features for our problem which, unexpectedly, are based on the number of unique users with whom the given user communicates around the Christmas and New Year's Eve holidays. In general, features based on the number of close ties maintained by a user perform better than others. Our resulting models significantly outperform the methods * Corresponding author currently published in the literature. The paper contributes also a systematic analysis of properties of the network derived from CDR.

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Credit scoring using three-way decisions with probabilistic rough sets

Cited by 78 publications

References 29 publications

Grey Fuzzy Multiple Attribute Group Decision-Making Methods Based on Interval Grey Triangular Fuzzy Numbers Partitioned Bonferroni Mean

Grey Fuzzy Multiple Attribute Group Decision-Making Methods Based on Interval Grey Triangular Fuzzy Numbers Partitioned Bonferroni Mean

Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers

Predicting complex user behavior from CDR based social networks

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