Results 41 to 50 of about 2,374,745 (297)
Improving Software Defect Prediction in Noisy Imbalanced Datasets
Applied Sciences, 2023 Software defect prediction is a popular method for optimizing software testing and improving software quality and reliability. However, software defect datasets usually have quality problems, such as class imbalance and data noise.
Haoxiang Shi +3 more
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Learning Imbalanced Datasets With Maximum Margin Loss
2021 IEEE International Conference on Image Processing (ICIP), 2021 A learning algorithm referred to as Maximum Margin (MM) is proposed for considering the class-imbalance data learning issue: the trained model tends to predict the majority of classes rather than the minority ones. That is, underfitting for minority classes seems to be one of the challenges of generalization.
Haeyong Kang, Thang Vu, Chang D. Yoo
openaire +2 more sources
Resampling imbalanced data for network intrusion detection datasets
Journal of Big Data, 2021 Machine learning plays an increasingly significant role in the building of Network Intrusion Detection Systems. However, machine learning models trained with imbalanced cybersecurity data cannot recognize minority data, hence attacks, effectively.
Sikha Bagui, Kunqi Li
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Machine Learning With Variational AutoEncoder for Imbalanced Datasets in Intrusion Detection
IEEE Access, 2022 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 three challenges: massive
Ying-Dar Lin +5 more
semanticscholar +1 more source
Undersampling Instance Selection for Hybrid and Incomplete Imbalanced Data [PDF]
Journal of Universal Computer Science, 2020 This paper proposes a novel undersampling method, for dealing with imbalanced datasets. The proposal is based on a novel instance importance measure (also introduced in this paper), and is able to balance hybrid and incomplete data.
Oscar Camacho-Nieto +2 more
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Superensemble classifier for improving predictions in imbalanced datasets [PDF]
Communications in Statistics: Case Studies, Data Analysis and Applications, 2020 Learning from an imbalanced dataset is a tricky proposition. Because these datasets are biased towards one class, most existing classifiers tend not to perform well on minority class examples. Conventional classifiers usually aim to optimize the overall accuracy without considering the relative distribution of each class.
Tanujit Chakraborty +1 more
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Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers [PDF]
International Journal of Computational Intelligence Systems, 2012 Determining whether an imprecise dataset is imbalanced is not immediate. The vagueness in the data causes that the prior probabilities of the classes are not precisely known, and therefore the degree of imbalance can also be uncertain.
AnaM. Palacios +2 more
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ITM Web of Conferences, 2017 The approach to the classification problem of the imbalanced datasets has been considered. The aim of this research is to determine the effectiveness of the SMOTE algorithm, when it is necessary to improve the classification quality of the SVM classifier,
Demidova Liliya, Klyueva Irina
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De ComputisCredit card fraud detection is a critical research area due to the significant financial losses and security risks associated with fraudulent activities.
Nazerke Baisholan +5 more
semanticscholar +1 more source
Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data
CAAI Transactions on Intelligence Technology, 2021 In the era of automatic task processing or designing complex algorithms, to analyse data, it is always pertinent to find real‐life solutions using cutting‐edge tools and techniques to generate insights into the data.
Gaurav Mohindru +2 more
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