DTO-SMOTE: Delaunay Tessellation Oversampling for Imbalanced Data Sets

Carvalho, A. M.; Prati, Ronaldo C.

doi:10.3390/info11120557

Cited by 5 publications

(3 citation statements)

References 51 publications

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“…To overcome this problem, the classical synthetic minority over‐sampling technique (SMOTE) 28 involves the generation of new samples without duplication but with the interpolation of a few neighboring samples, thus avoiding the problem of over‐fitting to some extent. Many variant algorithms derived from SMOTE, such as Borderline‐SMOTE 29,30 and K‐Means SMOTE, are also widely used in practical problems 31–33 . However, most of the above over‐sampling approaches to generate additional samples are based on subsets of minority samples and do not fully consider the overall distribution of data.…”

Section: The Latest Developments In Reliability Analysis For Complex ...mentioning

confidence: 99%

“…Many variant algorithms derived from SMOTE, such as Borderline-SMOTE 29,30 and K-Means SMOTE, are also widely used in practical problems. [31][32][33] However, most of the above over-sampling approaches to generate additional samples are based on subsets of minority samples and do not fully consider the overall distribution of data. Therefore, improvements in reliability performance are often limited.…”

Section: Probabilistic Metrics-oriented Ra Based On Data Augmentationmentioning

confidence: 99%

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Reliability analysis of complex electromechanical systems: State of the art, challenges, and prospects

Wang

Zhang

et al. 2022

Quality & Reliability Eng

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As the core foundation of reliability engineering and one of the most crucial aspects of general quality, reliability has been attracting increasing attention from industry and academia. Numerous publications on reliability analysis (RA) have appeared in academic journals, conference proceedings, and technical reports. However, there has not been a systematic summary that covers RA data, models, methods, indicators, and software comprehensively. To fill this gap, this paper analyzed the challenges and the state of the art, as well as determined the future prospects for research on RA. This paper introduces the features of modern complex electromechanical systems (CES), then summarizes the current challenges to the RA of CES. Next, it reviews the latest developments, such as reliability theories, models, methods, and software, then summarizes and compares their advantages and limitations. Finally, it discusses future prospects from the perspectives of reliability data, modeling, decoupling, evaluation indicators, and timely prediction. It is expected that this paper would serve as an introduction to RA for researchers new to this field and as a summary of the current frontiers of RA for experienced researchers.

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Section: The Latest Developments In Reliability Analysis For Complex ...mentioning

confidence: 99%

Section: Probabilistic Metrics-oriented Ra Based On Data Augmentationmentioning

confidence: 99%

Reliability analysis of complex electromechanical systems: State of the art, challenges, and prospects

Wang

Zhang

et al. 2022

Quality & Reliability Eng

View full text Add to dashboard Cite

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“…Finally, there are numerous studies using Voronoi diagrams to tackle imbalanced classification problems [ 55 , 56 , 57 ]. These kinds of problems arise when the distribution of examples among the classes is skewed.…”

Section: Related Workmentioning

confidence: 99%

Toward Accelerated Training of Parallel Support Vector Machines Based on Voronoi Diagrams

Alfaro

Gómez

Moguerza

et al. 2021

Entropy

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Typical applications of wireless sensor networks (WSN), such as in Industry 4.0 and smart cities, involves acquiring and processing large amounts of data in federated systems. Important challenges arise for machine learning algorithms in this scenario, such as reducing energy consumption and minimizing data exchange between devices in different zones. This paper introduces a novel method for accelerated training of parallel Support Vector Machines (pSVMs), based on ensembles, tailored to these kinds of problems. To achieve this, the training set is split into several Voronoi regions. These regions are small enough to permit faster parallel training of SVMs, reducing computational payload. Results from experiments comparing the proposed method with a single SVM and a standard ensemble of SVMs demonstrate that this approach can provide comparable performance while limiting the number of regions required to solve classification tasks. These advantages facilitate the development of energy-efficient policies in WSN.

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Fair XIDS: Ensuring fairness and transparency in intrusion detection models

Chinu,

Bansal

2024

Concurrency and Computation

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SummaryAn intrusion detection system (IDS) is valuable for detecting anomalies and unauthorized access to a system or network. Due to the black‐box nature of these IDS models, network experts need more trust in systems to act on alerts and transparency to understand the model's inner logic. Moreover, biased models' decisions affect the model performance and increase the false positive rates, directly affecting the model's accuracy. So, maintaining Transparency and Fairness simultaneously in IDS models is essential for accurate decision‐making. Existing methods face challenges of the tradeoff between fairness and accuracy, which also affects the reliability and robustness of the model. Motivated by these research gaps, we developed the Fair‐XIDS model. This model clarifies its internal logic with visual explanations and promotes fairness across its entire lifecycle. The Fair‐XIDS model successfully integrates complex transparency and fairness algorithms to address issues like Imbalanced datasets, algorithmic bias, and postprocessing bias with an average 85% reduction in false positive rate. To ensure reliability, the proposed model effectively mitigates the tradeoff between accuracy and fairness with an average of 90% accuracy and more than 85% fairness. The assessment results of the proposed model over diverse datasets and classifiers mark its model‐agnostic nature. Overall, the model achieves more than 85% consistency among diverse classifiers.

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DTO-SMOTE: Delaunay Tessellation Oversampling for Imbalanced Data Sets

Cited by 5 publications

References 51 publications

Reliability analysis of complex electromechanical systems: State of the art, challenges, and prospects

Reliability analysis of complex electromechanical systems: State of the art, challenges, and prospects

Toward Accelerated Training of Parallel Support Vector Machines Based on Voronoi Diagrams

Fair XIDS: Ensuring fairness and transparency in intrusion detection models

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