Clustering-based undersampling in class-imbalanced data

Lin, Wei; Tsai, Chih‐Fong; Hu, Yu; Jhang, Jing Shang

doi:10.1016/j.ins.2017.05.008

Cited by 624 publications

(289 citation statements)

References 26 publications

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“…The clustering-based undersampling (CUS) method [20] proved ineffective for the experimental dataset. All classifiers combined with CUS have not achieved good results.…”

Section: Bankruptcy Prediction Resultsmentioning

confidence: 99%

“…The proposed framework will be verified by the KBD dataset introduced in [28], which has a high balancing ratio. The experimental results of this study show that the proposed framework outperforms the GMBoost algorithm [24], the oversampling-based framework [28], and the clustering-based undersampling framework [20] for KBD. The main contributions of this study are highlighted as follows: (1) We propose the CBoost algorithm, which is a boosting algorithm with initial weight based on the clustering; (2) a robust framework using the CBoost algorithm and IHT (RFCI) is then proposed for effective bankruptcy prediction; (3) several experiments were conducted to find the optimal number of clusters using the Elbow method for KBD.…”

Section: Introductionmentioning

confidence: 86%

“…To show the effectiveness of the proposed framework, we perform the oversampling method using SMOTEENN [28] and the clustering-based undersampling method [20] combined with several classifiers such as MLP, Decision Tree, Random Forest, and AdaBoost; GMBoost [24] and the proposed framework for the experimental dataset. To evaluate these methods, the study uses 5-fold cross-validation, i.e., we used 80% of the dataset for training, leaving 20% for testing, and then repeated it five times.…”

Section: Experimental Settingmentioning

confidence: 99%

“…In the machine learning domain, classification is the problem of forecasting the class label a new observation belongs to. Classification is also known as supervised learning [20] and has attracted a lot of research attention; it can be applied to many practical applications in both research and industry. For example, credit card fraud detection [21] is a real-life classification problem.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, specific techniques such as undersampling as well as oversampling or a combination of them are needed. Lin et al [20] proposed an undersampling approach based on clustering for dealing with the imbalanced data problem and achieved promising results.…”

Section: Introductionmentioning

confidence: 99%

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A Cluster-Based Boosting Algorithm for Bankruptcy Prediction in a Highly Imbalanced Dataset

Sơn

et al. 2018

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Bankruptcy prediction has been a popular and challenging research topic in both computer science and economics due to its importance to financial institutions, fund managers, lenders, governments, as well as economic stakeholders in recent years. In a bankruptcy dataset, the problem of class imbalance, in which the number of bankruptcy companies is smaller than the number of normal companies, leads to a standard classification algorithm that does not work well. Therefore, this study proposes a cluster-based boosting algorithm as well as a robust framework using the CBoost algorithm and Instance Hardness Threshold (RFCI) for effective bankruptcy prediction of a financial dataset. This framework first resamples the imbalance dataset by the undersampling method using Instance Hardness Threshold (IHT), which is used to remove the noise instances having large IHT value in the majority class. Then, this study proposes a Cluster-based Boosting algorithm, namely CBoost, for dealing with the class imbalance. In this algorithm, the majority class will be clustered into a number of clusters. The distance from each sample to its closest centroid will be used to initialize its weight. This algorithm will perform several iterations for finding weak classifiers and combining them to create a strong classifier. The resample set resulting from the previous module, will be used to train CBoost, which will be used to predict bankruptcy for the validation set. The proposed framework is verified by the Korean bankruptcy dataset (KBD), which has a very small balancing ratio in both the training and the testing phases. The experimental results of this research show that the proposed framework achieves 86.8% in AUC (area under the ROC curve) and outperforms several methods for dealing with the imbalanced data problem for bankruptcy prediction such as GMBoost algorithm, the oversampling-based method using SMOTEENN, and the clustering-based undersampling method for bankruptcy prediction in the experimental dataset.

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“…The clustering-based undersampling (CUS) method [20] proved ineffective for the experimental dataset. All classifiers combined with CUS have not achieved good results.…”

Section: Bankruptcy Prediction Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 86%

Section: Experimental Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Cluster-Based Boosting Algorithm for Bankruptcy Prediction in a Highly Imbalanced Dataset

Sơn

et al. 2018

Symmetry

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Impact of Data Pre‐Processing in Information Retrieval for Data Analytics

Naz¹,

Ahuja²,

Nijhawan³

et al. 2023

Machine Intelligence, Big Data Analytics, and IoT in Image Processing

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A multi‐fault diagnosis method based on improved SMOTE for class‐imbalanced data

Zhao

et al. 2022

Can J Chem Eng

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With the development of industrial processes, how to effectively diagnose the faults in an increasingly complex production process has attracted widespread attention. It is worth noting that there may be multiple types of faults in the actual industrial process, and there is an extreme class imbalance between the normal samples and the fault samples. Therefore, it is of practical significance to carry out research on the multi-fault diagnosis method for class-imbalanced data. In this paper, a multi-fault diagnosis method based on improved synthetic minority sampling technology (SMOTE) is proposed. First, aiming at the class imbalance, an improved SMOTE algorithm based on Mahalanobis distance (Mahalanobis distance-based SMOTE [MSMOTE]) is proposed for over-

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Clustering-based undersampling in class-imbalanced data

Cited by 624 publications

References 26 publications

A Cluster-Based Boosting Algorithm for Bankruptcy Prediction in a Highly Imbalanced Dataset

A Cluster-Based Boosting Algorithm for Bankruptcy Prediction in a Highly Imbalanced Dataset

Impact of Data Pre‐Processing in Information Retrieval for Data Analytics

A multi‐fault diagnosis method based on improved SMOTE for class‐imbalanced data

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