Undersampling of approaching the classification boundary for imbalance problem

Jiang, Lei; Yuan, Peng; Liao, Jing; Zhang, Qiongbing; Liu, Jianxun; Li, Keqin

doi:10.1002/cpe.7586

Cited by 5 publications

(4 citation statements)

References 41 publications

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“…A healthy number of intrepid researchers have applied oversampling [9][10][11][12][13][14], undersampling [15][16][17][18][19], and hybrid [20][21][22][23] preprocessing methods to restore balance to their training datasets. These methods are combined with feature classification methods to maximize benefits.…”

Section: A Data-level Mitigation Effortsmentioning

confidence: 99%

Hybrid Intrusion Detection System Based on Data Resampling and Deep Learning

Chen,

You,

Shiue

2024

IJACSA

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The growth of the internet has advanced information-sharing capabilities and vastly increased the importance of global network security. However, because new and inconspicuous abnormal behaviors are nearly impossible to detect in massive network access environments, modern intrusion detection systems have identified a high rate of false-positive (FP) and false-negative (FN) attacks. To overcome this, this paper proposes a hybrid deep learning model that significantly mitigates the disadvantages of consistently imbalanced sample attack data. First, it resolves imbalanced data using random undersampling and synthetic minority oversampling techniques. Then, convolutional neural networks (CNNs) extract local and spatial features, and a transformer encoder extracts global and temporal features. The novelty of this combination increases recognition accuracy at the algorithm level, which is crucial to reducing FPs and FNs. The model was subjected to multiclassification testing on the NSL-KDD and CICIDS2017 benchmark datasets, and the results show that our model has higher classification accuracy and lower FP rates than state-ofthe-art intrusion detection models. Moreover, it significantly improves the detection rate of low-frequency attacks.

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Section: A Data-level Mitigation Effortsmentioning

confidence: 99%

Hybrid Intrusion Detection System Based on Data Resampling and Deep Learning

Chen,

You,

Shiue

2024

IJACSA

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“…The ensemble of the α− Trees framework (EAT) uses underbagging technology to achieve good results when applied to imbalanced classification problems [26]. In terms of boosting, the technologies commonly used include RUSBoost [27], SMOTEBoost [28], CSBBoost [29], Adaboost, and AsymBoost [30]. In addition, Yang et al [31] employed progressive density-based weighted ensemble learning, and Ren et al [32] designed a weighted integration scheme that was obtained via the use of classifiers based on the original imbalanced data set for ensemble learning.…”

Section: Ensemble Learning In Undersamplingmentioning

confidence: 99%

An Undersampling Method Approaching the Ideal Classification Boundary for Imbalance Problems

Zhou,

Liu,

Yuan

et al. 2024

Applied Sciences

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Data imbalance is a common problem in most practical classification applications of machine learning, and it may lead to classification results that are biased towards the majority class if not dealt with properly. An effective means of solving this problem is undersampling in the borderline area; however, it is difficult to find the area that fits the classification boundary. In this paper, we present a novel undersampling framework, whereby the clustering of samples in the majority class is conducted and segmentation is then performed in the boundary area according to the clusters obtained; this enables a better shape that fits the classification boundary to be obtained via the performance of random sampling in the borderline area of these segments. In addition, we hypothesize that there exists an optimal number of classifiers to be integrated into the method of ensemble learning that utilizes multiple classifiers that have been obtained via sampling to promote the algorithm. After passing the hypothesis test, we apply the improved algorithm to the newly developed method. The experimental results show that the proposed method works well.

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“…Combining these two methods can result in more accurate data for model building, which can improve the outcomes. The problem of data imbalance has been overlooked in petroleum engineering research, but it can cause results to favor the majority class [61]. However, the minority classes often require more accuracy.…”

Section: Potential Research Opportunitiesmentioning

confidence: 99%

Machine Learning in Reservoir Engineering: A Review

Zhou,

Liu,

Liu

et al. 2024

Processes

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With the rapid progress of big data and artificial intelligence, machine learning technologies such as learning and adaptive control have emerged as a research focus in petroleum engineering. They have various applications in oilfield development, such as parameter prediction, optimization scheme deployment, and performance evaluation. This paper provides a comprehensive review of these applications in three key scenarios of petroleum engineering, namely hydraulic fracturing and acidizing, chemical flooding and gas flooding, and water injection. This article first introduces the steps and methods of machine learning processing in these scenarios, then discusses the advantages, disadvantages, existing challenges, and future prospects of these machine learning methods. Furthermore, this article compares and contrasts the strengths and weaknesses of these machine learning methods, aiming to help researchers select and improve their methods. Finally, this paper identifies some potential development trends and research directions of machine learning in petroleum engineering based on the current issues.

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Undersampling of approaching the classification boundary for imbalance problem

Cited by 5 publications

References 41 publications

Hybrid Intrusion Detection System Based on Data Resampling and Deep Learning

Hybrid Intrusion Detection System Based on Data Resampling and Deep Learning

An Undersampling Method Approaching the Ideal Classification Boundary for Imbalance Problems

Machine Learning in Reservoir Engineering: A Review

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