Machine Learning With Variational AutoEncoder for Imbalanced Datasets in Intrusion Detection
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3149295
Abstract: As a result of the explosion of security attacks and the complexity of modern networks, machine learning (ML) has recently become the favored approach for intrusion detection systems (IDS). However, the ML approach usually faces… read more here.
Keywords: intrusion detection; imbalanced datasets; machine learning; detection ... See more keywords
A Practical Anonymization Approach for Imbalanced Datasets
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DOI: 10.1109/mitp.2021.3132330
Abstract: Person-specific data owned by different data holders is usually anonymized before being shared with researchers or data-miners. Anonymization is a pertinent solution for releasing useful information while ensuring privacy. Many anonymization approaches have been proposed… read more here.
Keywords: imbalanced datasets; anonymization; anonymization approach; practical anonymization ... See more keywords
Boosting methods for multi-class imbalanced data classification: an experimental review
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Big Data"
DOI: 10.1186/s40537-020-00349-y
Abstract: Since canonical machine learning algorithms assume that the dataset has equal number of samples in each class, binary classification became a very challenging task to discriminate the minority class samples efficiently in imbalanced datasets. For… read more here.
Keywords: class imbalanced; imbalanced data; classification; class ... See more keywords
An Enhanced MNB Based Model for Explicit and Hidden Sentiment Classification in Imbalanced Datasets
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Intelligent Engineering and Systems"
DOI: 10.22266/ijies2019.1031.08
Abstract: Sentiment polarity classification (either explicit or hidden) is the process by which information can be extracted to be analysed as positive or negative opinion. Much work on supervised machine learning based sentiment classification has been… read more here.
Keywords: classification; explicit hidden; imbalanced datasets; sentiment ... See more keywords
Online Batch Selection for Enhanced Generalization in Imbalanced Datasets
Sign Up to like & getrecommendations! Published in 2023 at "Algorithms"
DOI: 10.3390/a16020065
Abstract: Importance sampling, a variant of online sampling, is often used in neural network training to improve the learning process, and, in particular, the convergence speed of the model. We study, here, the performance of a… read more here.
Keywords: batch selection; imbalanced datasets; convergence; generalization ... See more keywords
An Asymmetric Contrastive Loss for Handling Imbalanced Datasets
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DOI: 10.3390/e24091303
Abstract: Contrastive learning is a representation learning method performed by contrasting a sample to other similar samples so that they are brought closely together, forming clusters in the feature space. The learning process is typically conducted… read more here.
Keywords: imbalanced datasets; handling imbalanced; loss; contrastive loss ... See more keywords
Analysis of Run-Off-Road Accidents by Association Rule Mining and Geographic Information System Techniques on Imbalanced Datasets
Sign Up to like & getrecommendations! Published in 2020 at "Sustainability"
DOI: 10.3390/su12124882
Abstract: Run-off-road (ROR) accidents cause a large proportion of fatalities on roads. Exploring key factors is an effective method to reduce fatalities and improve safety sustainability. However, some limitations exist in current studies: (1) Datasets of… read more here.
Keywords: imbalanced datasets; association rule; run road; ror accidents ... See more keywords
Adversarial Approaches to Tackle Imbalanced Data in Machine Learning
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DOI: 10.3390/su15097097
Abstract: Real-world applications often involve imbalanced datasets, which have different distributions of examples across various classes. When building a system that requires a high accuracy, the performance of the classifiers is crucial. However, imbalanced datasets can… read more here.
Keywords: imbalanced datasets; classification performance; classification; data augmentation ... See more keywords