ODBOT: Outlier detection-based oversampling technique for imbalanced datasets learning

Ibrahim, Mohammed H.

doi:10.1007/s00521-021-06198-x

Cited by 22 publications

(3 citation statements)

References 39 publications

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“…Other ways of oversampling include, but are not limited to, the work of [91,92,93,94,78,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119] The validation process is what all oversampling methods have in common, which is basically the evaluation of the classifier's performance employed to classify the oversampled datasets using one or more accuracy measures such as Accuracy, Precision, Recall, F-measure, G-mean, Specificity, Kappa, Matthews correlation coefficient (MCC), Area under the ROC Curve (AUC), True positive rate, False negative (FN), False positive (FP), True positive (TP), True negative (TN), and ROC curve. Table 1 lists 72 oversampling methods, including their known names, references, the number of datasets utilized, the number of classes in these datasets, the classifiers employed, and the performance metrics used to validate the classification results after oversampling.…”

Section: Literature Review Of Oversampling Methodsmentioning

confidence: 99%

Stop Oversampling for Class Imbalance Learning: A Critical Review

Hassanat¹,

Tarawneh²,

Altarawneh³

2022

Preprint

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For the last two decades, oversampling has been employed to overcome the challenge of learning from imbalanced datasets. Many approaches to solving this challenge have been offered in the literature. Oversampling, on the other hand, is a concern. That is, models trained on fictitious data may fail spectacularly when put to real-world problems. The fundamental difficulty with oversampling approaches is that, given a real-life population, the synthesized samples may not truly belong to the minority class. As a result, training a classifier on these samples while pretending they represent minority may result in incorrect predictions when the model is used in the real world. We analyzed a large number of oversampling methods in this paper and devised a new oversampling evaluation system based on hiding a number of majority examples and comparing them to those generated by the oversampling process. Based on our evaluation system, we ranked all these methods based on their incorrectly generated examples for comparison. Our experiments using more than 70 oversampling methods and three imbalanced real-world datasets reveal that all oversampling methods studied generate minority samples that are most likely to be majority. Given data and methods in hand, we argue that oversampling in its current forms and methodologies is unreliable for learning from class imbalanced data and should be avoided in real-world applications.

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Section: Literature Review Of Oversampling Methodsmentioning

confidence: 99%

Stop Oversampling for Class Imbalance Learning: A Critical Review

Hassanat¹,

Tarawneh²,

Altarawneh³

2022

Preprint

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“…The equations of the evaluation measurements: PRE, REC, SPE, F1-M, and AUC are given below as Equations 2, 3, 4, 5, and 6 respectively [9].…”

Section: Figure 1 Confusion Matrixmentioning

confidence: 99%

“…Recently, there is a large interest in the class imbalance topic by the researchers. Thus, it is considered a challenging issue and many techniques have been developed by researchers to solve this tricky problem [8,9]. Resampling techniques are considered one of the preprocessing techniques and commonly used approaches to making the dataset balanced [10].…”

Section: Introductionmentioning

confidence: 99%

A Salp Swarm-Based Under-Sampling Approach for Medical Imbalanced Data Classification

Ibrahim¹

2022

European Journal of Science and Technology

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Data imbalance refers to the unequal distribution of classes within a dataset that directly affects the accuracy of machine learning classification algorithms. Although many resampling techniques have been proposed by researchers, learning from imbalanced data is still considered one of the contemporary challenges. The class imbalanced problem has been complicated as most of the existing techniques don't manage the similarity relationships between minority and majority classes well. In addition, due to the complex relationships among classes, most of the existing techniques do not focus on retaining valuable samples in the majority class(es) properly. In this article, a salp swarm optimization-based under-sampling technique (SSBUT) is proposed to address data class imbalance problems. Utilizing the proposed SSBUT, the similarity relationship among the samples of the majority class is well analyzed, and the samples that do not affect the accuracy of the classification algorithm are eliminated from the majority class. The performance of the proposed SSBUT has been tested on benchmark medical imbalanced datasets and the obtained results have been compared with state-of-the-art under-sampling techniques. The experimental results show that the proposed SSBUT consistently outperformed the state-of-the-art under-sampling techniques in terms of various evaluation criteria.

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Detection of Shilling Attack with Support Vector Machines Using Oversampling

Ayaz,

Öztürk

2024

Communications in Computer and Information Science

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ODBOT: Outlier detection-based oversampling technique for imbalanced datasets learning

Cited by 22 publications

References 39 publications

Stop Oversampling for Class Imbalance Learning: A Critical Review

Stop Oversampling for Class Imbalance Learning: A Critical Review

A Salp Swarm-Based Under-Sampling Approach for Medical Imbalanced Data Classification

Detection of Shilling Attack with Support Vector Machines Using Oversampling

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