Classifier selection and clustering with fuzzy assignment in ensemble model for credit scoring

Zhang, Haoting; He, Hailin; Zhang, Wenyu

doi:10.1016/j.neucom.2018.07.070

Cited by 49 publications

(17 citation statements)

References 42 publications

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“…The use of a predictive tool could assist financial institutions to decide whether to grant credit to consumers who apply. Since our numerical experiments are quite encouraging, our future work is concentrated on evaluating the proposed algorithms versus relevant methodologies and frameworks addressing the credit score problem such as [27][28][29][30][31][32] and versus recently proposed advanced SSL algorithms such as [59][60][61].…”

Section: Discussionmentioning

confidence: 99%

“…Zhang et al [32] proposed a new predictive model which is based on a novel technique for selecting classifiers using a genetic algorithm, considering both the accuracy and diversity of the ensemble. They conducted a variety of experiments, using three real-world datasets (Australian credit scoring, Japanese credit scoring and German credit scoring) to explore the effectiveness of their proposed model.…”

Section: Related Workmentioning

confidence: 99%

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On Ensemble SSL Algorithms for Credit Scoring Problem

et al. 2018

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Credit scoring is generally recognized as one of the most significant operational research techniques used in banking and finance, aiming to identify whether a credit consumer belongs to either a legitimate or a suspicious customer group. With the vigorous development of the Internet and the widespread adoption of electronic records, banks and financial institutions have accumulated large repositories of labeled and mostly unlabeled data. Semi-supervised learning constitutes an appropriate machine- learning methodology for extracting useful knowledge from both labeled and unlabeled data. In this work, we evaluate the performance of two ensemble semi-supervised learning algorithms for the credit scoring problem. Our numerical experiments indicate that the proposed algorithms outperform their component semi-supervised learning algorithms, illustrating that reliable and robust prediction models could be developed by the adaptation of ensemble techniques in the semi-supervised learning framework.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

On Ensemble SSL Algorithms for Credit Scoring Problem

et al. 2018

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“…The first one, who dealt with forecasting financial crisis issues was Paul Joseph Fitzpatrick in 1932, who focused on essential differences between successful and failed companies whereby he used the ratio analysis to predict future bankruptcy [5,6]. Another huge improvement in the field of this area was done by Merwin (1942), when he compared the arithmetic means of selected corporate indicators in prosperous and non-prosperous business.…”

Section: Literature Reviewmentioning

confidence: 99%

The verification of prediction and classification ability of selected Slovak prediction models and their emplacement in forecasts of financial health of a company in aspect of globalization

Karpac

Bartošová

2020

SHS Web Conf.

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Predicting financial health of a company is in this global world necessary for each business entity, especially for the international ones, as it´s very important to know financial stability. Forecasting business failure is a worldwide known term, in a global notion, and there is a lot of prediction models constructed to compute financial health of a company and, by that, state whether a company inclines to financial boom or bankruptcy. Globalized prediction models compute financial health of companies, but the vast majority of models predicting business failure are constructed solely for the conditions of a particular country or even just for a specific sector of a national economy. Field of financial predictions regarding to international view consists of elementary used models, for example, such as Altman´s Z-score or Beerman´s index, which are globally know and used as basic of many other modificated models. Following article deals with selected Slovak prediction models designed to Slovak conditions, states how these models stand in this global world, what is their international connection to the worldwide economies, and also states verification of their prediction ability in a specific sector. The verification of predictive ability of the models is defined by ROC analysis and through results the paper demonstrates the most suitable prediction models to use in the selected sector.

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“…Therefore, to improve the overall ensemble learning performance, a balance between the diversity and accuracy of the learners must be established. Under the premise of considering accuracy and diversity, Zhang et al [35] propose a classifier selection method based on a genetic algorithm. They integrated unsupervised clustering with a fuzzy assignment process to make full use of data patterns to improve the ensemble performance.…”

Section: Introductionmentioning

confidence: 99%

“…In previous studies [35,36], when the ensemble learning model was built, although the objective function includes accuracy and diversity factors, the diversity factor is used as a regularization term to avoid overfitting. Nevertheless, the diversity factor considers only the prediction results of the classifier.…”

Section: Introductionmentioning

confidence: 99%

A Weighted Ensemble Learning Algorithm Based on Diversity Using a Novel Particle Swarm Optimization Approach

et al. 2020

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In ensemble learning, accuracy and diversity are the main factors affecting its performance. In previous studies, diversity was regarded only as a regularization term, which does not sufficiently indicate that diversity should implicitly be treated as an accuracy factor. In this study, a two-stage weighted ensemble learning method using the particle swarm optimization (PSO) algorithm is proposed to balance the diversity and accuracy in ensemble learning. The first stage is to enhance the diversity of the individual learner, which can be achieved by manipulating the datasets and the input features via a mixed-binary PSO algorithm to search for a set of individual learners with appropriate diversity. The purpose of the second stage is to improve the accuracy of the ensemble classifier using a weighted ensemble method that considers both diversity and accuracy. The set of weighted classifier ensembles is obtained by optimization via the PSO algorithm. The experimental results on 30 UCI datasets demonstrate that the proposed algorithm outperforms other state-of-the-art baselines.

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Classifier selection and clustering with fuzzy assignment in ensemble model for credit scoring

Cited by 49 publications

References 42 publications

On Ensemble SSL Algorithms for Credit Scoring Problem

On Ensemble SSL Algorithms for Credit Scoring Problem

The verification of prediction and classification ability of selected Slovak prediction models and their emplacement in forecasts of financial health of a company in aspect of globalization

A Weighted Ensemble Learning Algorithm Based on Diversity Using a Novel Particle Swarm Optimization Approach

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