On the Performance of Oversampling Techniques for Class Imbalance Problems

Kong, Jiawen; Rios, Thiago; Kowalczyk, Wojtek; Menzel, Stefan; Bäck, Thomas

doi:10.1007/978-3-030-47436-2_7

Cited by 12 publications

(4 citation statements)

References 19 publications

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“…Stateful authentication is particularly beneficial for ensuring secure access to servers through firewalls, enhancing efficiency by using tokens or similar methods for continuous authentication. It finds its application in various advanced fields, such as Ad hoc networks, Blockchain technology, and 5G networks [12], [28], [29], [30], [31]. Beyond its conventional use in logical security, No efforts were taken to extend stateful authentication to physical security measures, such as smart gates, aiming to boost efficiency without compromising safety.…”

Section: Efficient Authenticationmentioning

confidence: 99%

Efficient Entry: A Stateful Authentication Approach in Health-Aware Smart Gate Systems

Elhussein,

Almuhaideb,

Alholyal

et al. 2024

IEEE Access

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This study presents the development and evaluation of Health-Aware Smart Gates (HASG), designed to enhance public health security during the COVID-19 pandemic. When compared with traditional smart gate systems, HASGs offer improved security through rigorous health protocol enforcement, enhanced user experience by reducing wait times, and increased reliability. The innovation lies in its stateful authentication mechanism-a contrast to the conventional stateless systems-which reduces wait times for frequent users by 31.7%, significantly enhancing efficiency and security. This server-inspired approach allows HASGs to quickly adapt to various health emergencies and incorporate potential hardware upgrades, further reducing response times and elevating the technology's contribution to smart gate applications. Prototype testing confirms the system's high accuracy and reliability, underlining its value in smart gate technology.INDEX TERMS Biometrics (access control), COVID-19, Health and Safety, Internet of Things, Smart Healthcare.

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Section: Efficient Authenticationmentioning

confidence: 99%

Efficient Entry: A Stateful Authentication Approach in Health-Aware Smart Gate Systems

Elhussein,

Almuhaideb,

Alholyal

et al. 2024

IEEE Access

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“…As future work, other anomaly detection techniques, such as the clustering-based local outlier score (CBLOF) [8] and histogrambased outlier score (HBOS) [6] could be included in the analysis. Future work could also consider an extension of this research for engineering datasets [10], especially for the design optimization problems mentioned in our Introduction. Detailed analysis of the feature importance and how the proposed method affects the classification performance in the overlapping region would also be worth studying.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Improving Imbalanced Classification by Anomaly Detection

Kong

Kowalczyk

Menzel

et al. 2020

Parallel Problem Solving From Nature – PPSN XVI

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Although the anomaly detection problem can be considered as an extreme case of class imbalance problem, very few studies consider improving class imbalance classification with anomaly detection ideas. Most data-level approaches in the imbalanced learning domain aim to introduce more information to the original dataset by generating synthetic samples. However, in this paper, we gain additional information in another way, by introducing additional attributes. We propose to introduce the outlier score and four types of samples (safe, borderline, rare, outlier) as additional attributes in order to gain more information on the data characteristics and improve the classification performance. According to our experimental results, introducing additional attributes can improve the imbalanced classification performance in most cases (6 out of 7 datasets). Further study shows that this performance improvement is mainly contributed by a more accurate classification in the overlapping region of the two classes (majority and minority classes). The proposed idea of introducing additional attributes is simple to implement and can be combined with resampling techniques and other algorithmiclevel approaches in the imbalanced learning domain.

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“…In this scenario, oversampling techniques are used to generate synthetic samples that represent the original data distribution. Some of the commonly used oversampling techniques are described below SMOTE is one of the widely used oversampling techniques [38].…”

Section: Oversamplingmentioning

confidence: 99%

A Detailed Analysis of the Multi-Class Classification Problem in Network Intrusion Detection using Resampling Techniques

Ahsan¹

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Cybersecurity has recently become a significant concern. In particular, machine learning algorithms are known to help identify cyberattacks such as network intrusion.However, common network intrusion datasets are often imbalanced: the normal traffic behaviors constitute the majority of the dataset, while intrusion traffic behaviors form a significantly smaller portion. We conduct a detailed analysis on the impact of SVM Support Vector Machine WGAN-GP Wasserstein GAN with Gradient Penalty xv

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On the Performance of Oversampling Techniques for Class Imbalance Problems

Cited by 12 publications

References 19 publications

Efficient Entry: A Stateful Authentication Approach in Health-Aware Smart Gate Systems

Efficient Entry: A Stateful Authentication Approach in Health-Aware Smart Gate Systems

Improving Imbalanced Classification by Anomaly Detection

A Detailed Analysis of the Multi-Class Classification Problem in Network Intrusion Detection using Resampling Techniques

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