A feature selection enabled hybrid‐bagging algorithm for credit risk evaluation

Dahiya, Shashi; Handa, S. S.; Singh, N P

doi:10.1111/exsy.12217

Cited by 41 publications

(23 citation statements)

References 56 publications

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“…Real-time datasets are susceptible to various quality issues, such as missing values, different data structures, data redundancy, and imbalanced data [45]. Herein, standard preprocessing operations are applied to the data.…”

Section: Data Processing and Feature Extractionmentioning

confidence: 99%

A Trial of Student Self-Sponsored Peer-to-Peer Lending Based on Credit Evaluation Using Big Data Analysis

Hou

Mei

et al. 2019

Computational Intelligence and Neuroscience

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There is still no effective approach to overcome the problem of credit evaluation for Chinese students. In absence of a reliable credit evaluation system for students, the university students have to only apply through online peer-to-peer (P2P) loan platforms because Chinese financial institutions typically reject students’ loan applications. Lack of students’ financial records hinders financial institutes and banks to routinely evaluate the students’ credit status and assign loans to them. Hence, this paper attempted to benefit from university students’ diversified daily behavior data, and logistic regression (LR) and gradient boosting decision tree (GBDT) algorithms were also used to develop robust credit evaluation models for university students, in which the validation of the proposed models was assessed by a real-time P2P lending platform. In this study, the students’ overdue behavior in returning books to university library was used as an index. With training 17838 samples, the proposed models performed well, while GBDT-based model outperformed in identification of “bad borrowers.” Based on the proposed models, a self-sponsored peer-to-peer loan platform was established and developed in a Chinese university for ten months, and the achieved findings demonstrated that adopting such credit evaluation models can effectively reduce the default ratio.

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Section: Data Processing and Feature Extractionmentioning

confidence: 99%

A Trial of Student Self-Sponsored Peer-to-Peer Lending Based on Credit Evaluation Using Big Data Analysis

Hou

Mei

et al. 2019

Computational Intelligence and Neuroscience

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“…In a complete data set, ensemble approaches can improve the classification performance [21]. Bootstrapping or bagging is a popular ensemble learning approach where data is re-sampled with substitution several times [3,7]. The reason for good performance of bagging is that it creates multiple datasets which lead to diverse and accurate classifiers.…”

Section: Bagging and MI Ensemblementioning

confidence: 99%

Bootstrapping and multiple imputation ensemble approaches for classification problems

Khan

Ahmad

Mihailidis

2019

IFS

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Presence of missing values in a dataset can adversely affect the performance of a classifier. Single and Multiple Imputation are normally performed to fill in the missing values. In this paper, we present several variants of combining single and multiple imputation with bootstrapping to create ensembles that can model uncertainty and diversity in the data, and that are robust to high missingness in the data. We present three ensemble strategies: bootstrapping on incomplete data followed by (i) single imputation and (ii) multiple imputation, and (iii) multiple imputation ensemble without bootstrapping. We perform an extensive evaluation of the performance of the these ensemble strategies on eight datasets by varying the missingness ratio. Our results show that bootstrapping followed by multiple imputation using expectation maximization is the most robust method even at high missingness ratio (up to 30%). For small missingness ratio (up to 10%) most of the ensemble methods perform equivalently but better than single imputation. Kappa-error plots suggest that accurate classifiers with reasonable diversity is the reason for this behaviour. A consistent observation in all the datasets suggests that for small missingness (up to 10%), bootstrapping on incomplete data without any imputation produces equivalent results to other ensemble methods.

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“…Baesens (2003) compared various classifiers on eight data sets with 17 classifiers. Ensembles of classifiers also emerged as novel classification approaches and have since been improved and combined (Dahiya, Handa, & Singh, 2017). In another study, Baesens studied the new benchmarking analyses of credit scoring algorithms (Lessmann et al, 2015), including novel learning methods and new data sets.…”

Section: Literature Reviewmentioning

confidence: 99%

Credit scoring for a microcredit data set using the synthetic minority oversampling technique and ensemble classifiers

Gicić

Subaşı

2018

Expert Systems

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Although microfinance organizations play an important role in developing economies, decision support models for microfinance credit scoring have not been sufficiently covered in the literature, particularly for microcredit enterprises. The aim of this paper is to create a three‐class model that can improve credit risk assessment in the microfinance context. The real‐world microcredit data set used in this study includes data from retail, micro, and small enterprises. To the best of the authors' knowledge, existing research on microfinance credit scoring has been limited to regression and genetic algorithms, thereby excluding novel machine learning algorithms. The aim of this research is to close this gap. The proposed models predict default events by analysing different ensemble classification methods that empower the effects of the synthetic minority oversampling technique (SMOTE) used in the preprocessing of the imbalanced microcredit data set. Initial results have shown improvement in the prediction results for certain classes when the oversampling technique with homogeneous and heterogeneous ensemble classifier methods was applied. A prediction improvement for all classes was achieved via application of SMOTE and the Consolidated Trees Construction algorithm together with Rotation Forest. To obtain a complete view of all aspects, an additional set of metrics is used in the evaluation of performance.

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A feature selection enabled hybrid‐bagging algorithm for credit risk evaluation

Cited by 41 publications

References 56 publications

A Trial of Student Self-Sponsored Peer-to-Peer Lending Based on Credit Evaluation Using Big Data Analysis

A Trial of Student Self-Sponsored Peer-to-Peer Lending Based on Credit Evaluation Using Big Data Analysis

Bootstrapping and multiple imputation ensemble approaches for classification problems

Credit scoring for a microcredit data set using the synthetic minority oversampling technique and ensemble classifiers

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