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An Oversampling Technique by Integrating Reverse Nearest Neighbor in SMOTE: Reverse-SMOTE
2020 International Conference on Smart Electronics and Communication (ICOSEC), 2020In recent years, the classification problem of an imbalanced dataset is getting a high demand in the field of machine learning. The SMOTE (Synthetic Minority Oversampling Technique) is a traditional approach to solve this issue. The main drawback of SMOTE is the issue of overfitting, as it randomly synthesized the minority data samples taking no notice
Riju Das +3 more
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Proceedings of the 2017 International Conference on Information Technology, 2017
In recent years, classification of imbalanced data has troubled most classification models because of the imbalanced class distribution. Synthetic Minority Oversampling Technique (SMOTE) is one of the solutions at data level, but this kind of method doesn't consider the distribution of the data set, thus the result is not satisfied.
Cheng Zhang +3 more
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In recent years, classification of imbalanced data has troubled most classification models because of the imbalanced class distribution. Synthetic Minority Oversampling Technique (SMOTE) is one of the solutions at data level, but this kind of method doesn't consider the distribution of the data set, thus the result is not satisfied.
Cheng Zhang +3 more
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SMOTE Inspired Extension for Differential Evolution
2022Although differential evolution (DE) is a well established optimisation method, proven on a wide variety of problems, modifications are proposed on a regular basis attempting to ever more improve its performance. Typical avenues for improvement include the introduction of new (mutation) operators or parameter control schemes.
Bajer, Dražen +2 more
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Farthest SMOTE: A Modified SMOTE Approach
2018Class imbalance problem comprises of uneven distribution of data/instances in classes which poses a challenge in the performance of classification models. Traditional classification algorithms produce high accuracy rate for majority classes and less accuracy rate for minority classes. Study of such problem is called class imbalance learning.
Anjana Gosain, Saanchi Sardana
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SMOTE-D a Deterministic Version of SMOTE
2016Imbalanced data is a problem of current research interest. This problem arises when the number of objects in a class is much lower than in other classes. In order to address this problem several methods for oversampling the minority class have been proposed.
Fredy Rodríguez Torres +2 more
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2014 International Conference on Advanced Computer Science and Information System, 2014
The imbalanced dataset often becomes obstacle in supervised learning process. Imbalance is case in which the example in training data belonging to one class is heavily outnumber the examples in the other class. Applying classifier to this dataset results in the failure of classifier to learn the minority class. Synthetic Minority Oversampling Technique
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The imbalanced dataset often becomes obstacle in supervised learning process. Imbalance is case in which the example in training data belonging to one class is heavily outnumber the examples in the other class. Applying classifier to this dataset results in the failure of classifier to learn the minority class. Synthetic Minority Oversampling Technique
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The Application of SMOTE Algorithm for Unbalanced Data
Proceedings of the 2018 International Conference on Artificial Intelligence and Virtual Reality, 2018The current power user data is unbalanced when it is used to analyze the behavior of the leakage user. In other words, the normal user data and the leakage user data have an inconsistent scale. When the automatic identification model of the leakage user is established, the analysis of the information of the leakage user's behavior feature is not clear,
Dong Lv +5 more
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Deterministic oversampling methods based on SMOTE
Journal of Intelligent & Fuzzy Systems, 2019In supervised classification if one of the classes has fewer objects than the other, we have a class imbalance problem. One of the most common solutions to address class imbalance problems is oversampling, and SMOTE is the most referenced and well-known oversampling method. However, SMOTE creates synthetic objects in a random way, therefore it produces
Fredy Rodríguez Torres +2 more
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Automatic Determination of Neighborhood Size in SMOTE
Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication, 2016In order to handle the class imbalance problem, synthetic data generation methods such as SMOTE, ADASYN, and Borderline-SMOTE have been developed. These methods use a common parameter k, the number of nearest neighbors. Nonetheless the most effective k value depends on the given dataset, there is no guideline to determine k.
Jaesub Yun, Jihyun Ha, Jong-Seok Lee
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Weighted-SMOTE: A modification to SMOTE for event classification in sodium cooled fast reactors
Progress in Nuclear Energy, 2017Abstract Traditionally, the plight of imbalanced dataset and its classification quandary has been counteracted mostly using under-sampling, over-sampling or ensemble sampling methods. Among these algorithms, Synthetic Minority Over-sampling Technique (SMOTE) which belongs to oversampling method has had lot of admiration and extensive range of ...
Manas Ranjan Prusty +2 more
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