Dynamic ensemble classification for credit scoring using soft probability

Feng, Xiaodong; Zhong, Bo; Qiu, Jing; Dong, Yuanxiang

doi:10.1016/j.asoc.2018.01.021

Cited by 100 publications

(58 citation statements)

References 54 publications

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“…The gradient loss of −1 on the training, data has been determined by (12). The negative gradient of the next classifier has also been determined by (12).…”

Section: Gradient Boostingmentioning

confidence: 99%

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An Investigation of Credit Card Default Prediction in the Imbalanced Datasets

et al. 2020

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Financial threats are displaying a trend about the credit risk of commercial banks as the incredible improvement in the financial industry has arisen. In this way, one of the biggest threats faces by commercial banks is the risk prediction of credit clients. Recent studies mostly focus on enhancing the classifier performance for credit card default prediction rather than an interpretable model. In classification problems, an imbalanced dataset is also crucial to improve the performance of the model because most of the cases lied in one class, and only a few examples are in other categories. Traditional statistical approaches are not suitable to deal with imbalanced data. In this study, a model is developed for credit default prediction by employing various credit-related datasets. There is often a significant difference between the minimum and maximum values in different features, so Min-Max normalization is used to scale the features within one range. Data level resampling techniques are employed to overcome the problem of the data imbalance. Various undersampling and oversampling methods are used to resolve the issue of class imbalance. Different machine learning models are also employed to obtain efficient results. We developed the hypothesis of whether developed models using different machine learning techniques are significantly the same or different and whether resampling techniques significantly improves the performance of the proposed models. Oneway Analysis of Variance is a hypothesis-testing technique, used to test the significance of the results. The split method is utilized to validate the results in which data has split into training and test sets. The results on imbalanced datasets show the accuracy of 66.9% on Taiwan clients credit dataset, 70.7% on South German clients credit dataset, and 65% on Belgium clients credit dataset. Conversely, the results using our proposed methods significantly improve the accuracy of 89% on Taiwan clients credit dataset, 84.6% on South German clients credit dataset, and 87.1% on Belgium clients credit dataset. The results show that the performance of classifiers is better on the balanced dataset as compared to the imbalanced dataset. It is also observed that the performance of data oversampling techniques are better than undersampling techniques. Overall, the Gradient Boosted Decision Tree method performs better than other traditional machine learning classifiers. The Gradient Boosted Decision Tree method gives the best results while utilizing the K-means SMOTE oversampling method. Using one-way ANOVA, the null hypothesis was rejected by a p-value <0.001, hence confirming that the proposed model improved performance is statistical significance. The interpretable model is also deployed on the web to ease the different stakeholders. This model will help commercial banks, financial organizations, loan institutes, and other decision-makers to predict the loan defaulter earlier.

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“…The gradient loss of −1 on the training, data has been determined by (12). The negative gradient of the next classifier has also been determined by (12).…”

Section: Gradient Boostingmentioning

confidence: 99%

“…The indicators or features related to predicting the credit default are still questionable and also alternatively changed in the past years. Hence, the traditional statistical were not able to solve the problem, and there is a dire need to build a machine learning model to predict the credit default effectively of the client [12].…”

Section: Introductionmentioning

confidence: 99%

An Investigation of Credit Card Default Prediction in the Imbalanced Datasets

et al. 2020

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“…There was one problem with the initial idea of applying ML in IDS, that is, a single classifier may not be strong enough to build a good IDS. Thus, researchers have come up with the idea of constructing ensemble classifiers for IDSs [28,77]. In general, the main goal of ensemble learning is to combine a set of individual classifiers and then make a better classification decision about the object submitted at the input [72].…”

Section: Introductionmentioning

confidence: 99%

Building an efficient intrusion detection system based on feature selection and ensemble classifier

et al. 2020

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A B S T R A C T Intrusion detection system (IDS) is one of extensively used techniques in a network topology to safeguard the integrity and availability of sensitive assets in the protected systems. Although many supervised and unsupervised learning approaches from the field of machine learning have been used to increase the efficacy of IDSs, it is still a problem for existing intrusion detection algorithms to achieve good performance. First, lots of redundant and irrelevant data in high-dimensional datasets interfere with the classification process of an IDS. Second, an individual classifier may not perform well in the detection of each type of attacks. Third, many models are built for stale datasets, making them less adaptable for novel attacks. Thus, we propose a new intrusion detection framework in this paper, and this framework is based on the feature selection and ensemble learning techniques. In the first step, a heuristic algorithm called CFS-BA is proposed for dimensionality reduction, which selects the optimal subset based on the correlation between features. Then, we introduce an ensemble approach that combines C4.5, Random Forest (RF), and Forest by Penalizing Attributes (Forest PA) algorithms. Finally, voting technique is used to combine the probability distributions of the base learners for attack recognition. The experimental results, using NSL-KDD, AWID, and CIC-IDS2017 datasets, reveal that the proposed CFS-BA-Ensemble method is able to exhibit better performance than other related and state of the art approaches under several metrics.

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“…A lot of research effort has been committed to evaluating classification algorithms in credit scoring, ranging from traditional statistical methods, such as logistic regression [1], to non-parametric algorithms, such as neural networks [9]. In the recent years there has been an increased interest for using hybrid and ensemble classifiers in credit risk, such as boosted regression trees, random forests, deep learning methods and other [10], [11], [12], [13] [14]. A number of benchmark studies have been performed, comparing classification accuracy of different classification algorithms [15], [8].…”

Section: Background Workmentioning

confidence: 99%

An insight into the effects of class imbalance and sampling on classification accuracy in credit risk assessment

Andric¹,

Kalpić²,

Bohaček³

2019

COMSIS J

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In this paper we investigate the role of sample size and class distribution in credit risk assessments, focusing on real life imbalanced data sets. Choosing the optimal sample is of utmost importance for the quality of predictive models and has become an increasingly important topic with the recent advances in automating lending decision processes and the ever growing richness in data collected by financial institutions. To address the observed research gap, a large-scale experimental evaluation of real-life data sets of different characteristics was performed, using several classification algorithms and performance measures. Results indicate that various factors play a role in determining the optimal class distribution, namely the performance measure, classification algorithm and data set characteristics. The study also provides valuable insight on how to design the training sample to maximize prediction performance and the suitability of using different classification algorithms by assessing their sensitivity to class imbalance and sample size.

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Dynamic ensemble classification for credit scoring using soft probability

Cited by 100 publications

References 54 publications

An Investigation of Credit Card Default Prediction in the Imbalanced Datasets

An Investigation of Credit Card Default Prediction in the Imbalanced Datasets

Building an efficient intrusion detection system based on feature selection and ensemble classifier

An insight into the effects of class imbalance and sampling on classification accuracy in credit risk assessment

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