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Adaptive neighbor synthetic minority oversampling technique under 1NN outcast handling [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2017 SMOTE is an effective oversampling technique for a class imbalance problem due to its simplicity and relatively high recall value. One drawback of SMOTE is a requirement of the number of nearest neighbors as a key parameter to synthesize instances ...
Wacharasak Siriseriwan +1 more
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A Hybrid GAN-Based Approach to Solve Imbalanced Data Problem in Recommendation Systems
IEEE Access, 2022 With the advent of information technology, the amount of online data generation has been massive. Recommendation systems have become an effective tool in filtering information and solving the problem of information overload.
Wafa Shafqat, Yung-Cheol Byun
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Frontiers in Materials, 2022 Digital twins in the mechanics of materials usually involve multimodal data in the sense that an instance of a mechanical component has both experimental and simulated data.
Axel Aublet +4 more
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Deep Learning-Based Imbalanced Classification With Fuzzy Support Vector Machine
Frontiers in Bioengineering and Biotechnology, 2022 Imbalanced classification is widespread in the fields of medical diagnosis, biomedicine, smart city and Internet of Things. The imbalance of data distribution makes traditional classification methods more biased towards majority classes and ignores the ...
Ke-Fan Wang +6 more
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Applied Sciences, 2019 A dropout early warning system enables schools to preemptively identify students who are at risk of dropping out of school, to promptly react to them, and eventually to help potential dropout students to continue their learning for a better future ...
Sunbok Lee, Jae Young Chung
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AE-CGAN Model based High Performance Network Intrusion Detection System
Applied Sciences, 2019 In this paper, a high-performance network intrusion detection system based on deep learning is proposed for situations in which there are significant imbalances between normal and abnormal traffic.
JooHwa Lee, KeeHyun Park
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Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
Applied Sciences, 2020 Imbalanced data are a major factor for degrading the performance of software defect models. Software defect dataset is imbalanced in nature, i.e., the number of non-defect-prone modules is far more than that of defect-prone ones, which results in the ...
Haonan Tong, Shihai Wang, Guangling Li
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Annals of Geophysics, 2000 The Center for Marine Resources and Environmental Technology has been developing a new method to improve the resolution of high-resolution seismic profiling.
C. R. Partouche
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Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2020 Oversampling is a technique to balance the number of data records for each class by generating data with a small number of records in a class, so that the amount is balanced with data with a class with a large number of records.
Nurul Chamidah +2 more
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Imbalanced Data Classification Method Based on LSSASMOTE
IEEE Access, 2023 Imbalanced data exist extensively in the real world, and the classification of imbalanced data is a hot topic in machine learning. In order to classify imbalanced data more effectively, an oversampling method named LSSASMOTE is proposed in this paper ...
Zhi Wang, Qicheng Liu
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