Evolutionary Ensemble Approach for Behavioral Credit Scoring

Nikitin, Nikolay O.; Kalyuzhnaya, Anna V.; Bochenina, Klavdiya; Kudryashov, Alexander A.; Uteuov, Amir; Derevitskii, Ivan; Boukhanovsky, Alexander V.

doi:10.1007/978-3-319-93713-7_81

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…A cooperative training algorithm showed the potential to improve the generalization ability. An evolutionary approach was used in [ 61 ] to build an ensemble structure, i.e., to compose optimal sets of models and input variables. Evolutionary-controlled ensembles provide better performance than a single model learning in scoring predictions.…”

Section: State Of the Artmentioning

confidence: 99%

Emerging Complexity in Distributed Intelligent Systems

Guleva

Shikov

Bochenina

et al. 2020

Entropy

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Distributed intelligent systems (DIS) appear where natural intelligence agents (humans) and artificial intelligence agents (algorithms) interact, exchanging data and decisions and learning how to evolve toward a better quality of solutions. The networked dynamics of distributed natural and artificial intelligence agents leads to emerging complexity different from the ones observed before. In this study, we review and systematize different approaches in the distributed intelligence field, including the quantum domain. A definition and mathematical model of DIS (as a new class of systems) and its components, including a general model of DIS dynamics, are introduced. In particular, the suggested new model of DIS contains both natural (humans) and artificial (computer programs, chatbots, etc.) intelligence agents, which take into account their interactions and communications. We present the case study of domain-oriented DIS based on different agents’ classes and show that DIS dynamics shows complexity effects observed in other well-studied complex systems. We examine our model by means of the platform of personal self-adaptive educational assistants (avatars), especially designed in our University. Avatars interact with each other and with their owners. Our experiment allows finding an answer to the vital question: How quickly will DIS adapt to owners’ preferences so that they are satisfied? We introduce and examine in detail learning time as a function of network topology. We have shown that DIS has an intrinsic source of complexity that needs to be addressed while developing predictable and trustworthy systems of natural and artificial intelligence agents. Remarkably, our research and findings promoted the improvement of the educational process at our university in the presence of COVID-19 pandemic conditions.

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Section: State Of the Artmentioning

confidence: 99%

Emerging Complexity in Distributed Intelligent Systems

Guleva

Shikov

Bochenina

et al. 2020

Entropy

Self Cite

View full text Add to dashboard Cite

show abstract

“…Data with unbalanced classes usually have classification problems in Machine Learning because the amount of data per class is not evenly distributed. Thoise conditions are usually found in credit, medicalhealth, and other data [7]. There are many approaches that have been The researchers employed many approaches to cope with the unbalanced class problemss.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Customer Loans Prediction Accuracy in Neural Networks

Syah¹,

Amron²,

Subagyo³

2023

Proceedings of the International Conference on Economic, Management, Business and Accounting, ICEMBA 2022, 17 December 2022, Ta

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Credit screening is divided into two categories of credit applicants based on their potential repayment capacity. A suitable applicants will be accepted high probability of default on installment obligations. Predicting creditworthiness is one of the considerations in granting credit to customers. Therefore the prediction accuracy results are needed. The neural network algorithm integrated use bagging and feature selection Support Vector Machine Recursive Feature Elimination (SVM-RFE). This study showed that this method improved the accuracy of predictive model performances. The lowest accuracy value was 86.52%. While 87.15% using the neural network model, joint bagging and optimizing the SVM-RFE selection. The experiment results revealed that the neural network model with bagging and optimizing the SVM-RFE selection could improve the performance of at customer credit prediction model.

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Metaheuristic-based ensemble learning: an extensive review of methods and applications

Rezk,

Selim

2024

Neural Comput & Applic

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Ensemble learning has become a cornerstone in various classification and regression tasks, leveraging its robust learning capacity across disciplines. However, the computational time and memory constraints associated with almost all-learners-based ensembles necessitate efficient approaches. Ensemble pruning, a crucial step, involves selecting a subset of base learners to address these limitations. This study underscores the significance of optimization-based methods in ensemble pruning, with a specific focus on metaheuristics as high-level problem-solving techniques. It reviews the intersection of ensemble learning and metaheuristics, specifically in the context of selective ensembles, marking a unique contribution in this direction of research. Through categorizing metaheuristic-based selective ensembles, identifying their frequently used algorithms and software programs, and highlighting their uses across diverse application domains, this research serves as a comprehensive resource for researchers and offers insights into recent developments and applications. Also, by addressing pivotal research gaps, the study identifies exploring selective ensemble techniques for cluster analysis, investigating cutting-edge metaheuristics and hybrid multi-class models, and optimizing ensemble size as well as hyper-parameters within metaheuristic iterations as prospective research directions. These directions offer a robust roadmap for advancing the understanding and application of metaheuristic-based selective ensembles.

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Evolutionary Ensemble Approach for Behavioral Credit Scoring

Cited by 6 publications

References 5 publications

Emerging Complexity in Distributed Intelligent Systems

Emerging Complexity in Distributed Intelligent Systems

Improvement of Customer Loans Prediction Accuracy in Neural Networks

Metaheuristic-based ensemble learning: an extensive review of methods and applications

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