RHSBoost: Improving classification performance in imbalance data

Gong, Joonho; Kim, Hyun‐Joong

doi:10.1016/j.csda.2017.01.005

Cited by 102 publications

(41 citation statements)

References 11 publications

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“…Receiver operating characteristics (ROC), G-mean, and F-measure (FM) are preferred as the likelihood these measures will be affected by imbalanced class distributions is low [11]. In a binary classification problem with good and bad class labels, the classifier's result is considered successful if both the false positive rate and false negative rate are small [15]. Sensitivity measures the accuracy of positive samples and specificity measures negative samples.…”

Section: Performance Measurementmentioning

confidence: 99%

Credit risk prediction in an imbalanced social lending environment

Namvar¹,

Siami²,

Rabhi³

et al. 2018

IJCIS

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Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique to use with imbalanced data is still controversial. In an attempt to address these problems, this paper presents an empirical comparison of various combinations of classifiers and resampling techniques within a novel risk assessment methodology that incorporates imbalanced data. The credit predictions from each combination are evaluated with a G-mean measure to avoid bias towards the majority class, which has not been considered in similar studies. The results reveal that combining random forest and random under-sampling may be an effective strategy for calculating the credit risk associated with loan applicants in social lending markets.

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Section: Performance Measurementmentioning

confidence: 99%

Credit risk prediction in an imbalanced social lending environment

Namvar¹,

Siami²,

Rabhi³

et al. 2018

IJCIS

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show abstract

“…Wang et al [8] used a K-labelsets ensemble method based on mutual information and joint entropy to deal with inblanced data. Gong et al [9] presented a ensemble method using random undersampling and ROSE sampling to solve the imbalance classification problem. So when we face the data imbalance problem, it's a good choice to determine data distribution or data label imbalance, and then apply these corresponding methods.…”

Section: Data Imbalancementioning

confidence: 99%

Urban flow prediction from spatiotemporal data using machine learning: A survey

et al. 2020

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“…The analysis of SMOTEBagging with logistic regression using credit scoring data revealed its higher degree of accuracy compared to a simple logistic algorithm (Hanifah et al, 2015). A new ensemble classification method using random undersampling and ROSE sampling under a boosting scheme RHSBoost was proposed to address the imbalance classification problem (Gong & Kim, 2017). A study proposed the variants of SMOTEBoost for imbalanced regression task and evaluated its performance using 30 datasets (Moniz et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

Distinct Multiple Learner-Based Ensemble SMOTEBagging (ML-ESB) Method for Classification of Binary Class Imbalance Problems

Sisodia¹,

Verma²

2019

IJTech

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Traditional classification algorithms often fail in learning from highly imbalanced datasets because the training involves most of the samples from majority class compared to the other existing minority class. In this paper, a Multiple Learners-based Ensemble SMOTEBagging (ML-ESB) technique is proposed. The ML-ESB algorithm is a modified SMOTEBagging technique in which the ensemble of multiple instances of the single learner is replaced by multiple distinct classifiers. The proposed ML-ESB is designed for handling only the binary class imbalance problem. In ML-ESB the ensembles of multiple distinct classifiers include Naïve Bays, Support Vector Machine, Logistic Regression and Decision Tree (C4.5) is used. The performance of ML-ESB is evaluated based on six binary imbalanced benchmark datasets using evaluation measures such as specificity, sensitivity, and area under receiver operating curve. The obtained results are compared with those of SMOTEBagging, SMOTEBoost, and cost-sensitive MCS algorithms with different imbalance ratios (IR). The ML-ESB algorithm outperformed other existing methods on four datasets with high dimensions and class IR, whereas it showed moderate performance on the remaining two low dimensions and small IR value datasets.

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RHSBoost: Improving classification performance in imbalance data

Cited by 102 publications

References 11 publications

Credit risk prediction in an imbalanced social lending environment

Credit risk prediction in an imbalanced social lending environment

Urban flow prediction from spatiotemporal data using machine learning: A survey

Distinct Multiple Learner-Based Ensemble SMOTEBagging (ML-ESB) Method for Classification of Binary Class Imbalance Problems

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