Machine Learning on imbalanced data in Credit Risk

Birla, Shiivong; Kohli, Kashish; Dutta, Akash

doi:10.1109/iemcon.2016.7746326

Cited by 16 publications

(11 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Financial risk prediction has been a hot topic for years due to its great importance [1]- [4]. Bankruptcy or default prediction is one of the most important tasks in financial risk management.…”

Section: Introductionmentioning

confidence: 99%

A MCDM-Based Evaluation Approach for Imbalanced Classification Methods in Financial Risk Prediction

Song

Peng

2019

IEEE Access

View full text Add to dashboard Cite

Various classifiers have been proposed for financial risk prediction. The traditional practice of using a singular performance metric for classifier evaluation is not sufficient for imbalanced classification. This paper proposes a multi-criteria decision making (MCDM)-based approach to evaluate imbalanced classifiers in credit and bankruptcy risk prediction by considering multiple performance metrics simultaneously. An experimental study is designed to provide a comprehensive evaluation of imbalanced classifiers using the proposed evaluation approach over seven financial imbalanced data sets from the UCI Machine Learning Repository. The TOPSIS, a well-known MCDM method, was applied to rank three categories of imbalanced classifiers using six popular evaluation criteria. The rankings results indicate that: 1) the rankings generated by the TOPSIS, which combine the results of six evaluation criteria, provide a more reasonable evaluation of imbalanced classifiers over any single performance criterion; and 2) Synthetic Minority Oversampling Technique (SMOTE)-based ensemble techniques outperform other groups of imbalanced learning approaches. Specifically, SMOTEBoost-C4.5, SMOTE-C4.5, and SMOTE-MLP were ranked as the top three classifiers based on their performances on the six criteria. INDEX TERMS Financial risk prediction, imbalanced classification, multiple criteria decision making (MCDM), algorithm evaluation.

show abstract

Section: Introductionmentioning

confidence: 99%

A MCDM-Based Evaluation Approach for Imbalanced Classification Methods in Financial Risk Prediction

Song

Peng

2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…This is due to the skewed class distribution leading to under-represented information associated with the minority class(es) (He and Garcia 2009). Examples of imbalanced data problems include sentiment analysis (Xu et al 2015), natural language processing and text mining (Li et al 2010), software fault detection (Malhotra 2015), medical diagnosis including cancer identification (Krawczyk et al 2016), credit risk and loan defaults (Birla et al 2016), and fault diagnostic in condition-based maintenance (Lee et al 2016). Evidently, the wide range of industries where imbalanced data challenges are applicable further emphasises the importance of addressing this issue in the context of decision support to ensure better preparedness for uncertainties during Black Swan events.…”

Section: Imbalanced Datamentioning

confidence: 99%

Artificial intelligence-based human-centric decision support framework: an application to predictive maintenance in asset management under pandemic environments

et al. 2021

View full text Add to dashboard Cite

“…Machine learning methods are defined under data‐driven methods, which solve the problem by using past experienced data. There are several fields where machine learning methods are used as a core technology such as image recognition, 32 the financial sector, 33 the healthcare sector, 34 etc. Several researchers have also found the importance of these methods for estimating the battery states.…”

Section: Introductionmentioning

confidence: 99%

Machinelearning‐basedapproach for useful capacity prediction ofsecond‐lifebatteries employing appropriate input selection

Bhatt

Ongsakul²,

Madhu

et al. 2021

Intl J of Energy Research

View full text Add to dashboard Cite

Electric vehicle-discarded second-life batteries still contain 80% of usable capacity and can serve as a low-cost alternative for microgrid storage applications where the battery storage capacity is flexible against transport applications. By accurately predicting the remaining useful capacity or state of health of these batteries, using the data from their first life operation, their costeffectiveness for microgrid energy management can be analyzed. For this purpose, three machine learning models are proposed here. The input parameters for the models are selected from the charging and discharging profiles of batteries, considering both the aging and regeneration phenomenon. Eight different input cases with and without K-fold (K = 10) cross-validation are used for training the proposed models. Based on the comparative analysis it is found that all the models trained with K-fold cross-validation, show minimum error as compared to without K-fold. The forecasting results from multiple approaches showed that the long short term memory model trained with battery discharging profile outperformed other models, quantified by multiple error indices, including root mean square error (0.009147), mean absolute error (0.005841), and R 2 (0.9713). The robustness of the model is validated with multiple battery datasets. Further, the study illustrates the importance of activation functions, in machine learning models used for forecasting.

show abstract

Machine Learning on imbalanced data in Credit Risk

Cited by 16 publications

References 4 publications

A MCDM-Based Evaluation Approach for Imbalanced Classification Methods in Financial Risk Prediction

A MCDM-Based Evaluation Approach for Imbalanced Classification Methods in Financial Risk Prediction

Artificial intelligence-based human-centric decision support framework: an application to predictive maintenance in asset management under pandemic environments

Machinelearning‐basedapproach for useful capacity prediction ofsecond‐lifebatteries employing appropriate input selection

Contact Info

Product

Resources

About