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Intrusion detection with HACDT-Net and TRBM-Net using a hybrid deep learning framework with enhanced sampling techniques. [PDF]
Sci RepPadma Priya N, Mohanbabu G.
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PF-SMOTE: A novel parameter-free SMOTE for imbalanced datasets
Neurocomputing, 2022Zhang Zhongliang, X G Luo, Qiong Chen
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Optimal Entropy Genetic Fuzzy-C-Means SMOTE (OEGFCM-SMOTE)
Knowledge-Based Systems, 2023El Moutaouakil Karim +1 more
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SMOTE-Text: A Modified SMOTE for Turkish Text Classification
2021One of the most common problems faced by large enterprise companies is the loss of knowhow after employee’s job replacements and quits. Creating a well-organized, indexed, connected, user friendly and sustainable digital enterprise memory can solve this problem and creates a practical knowhow transfer to new recruited personnel.
Nur Curukoglu, Alper Ozpinar
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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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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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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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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 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.
Drazen Bajer, Bruno Zoric, Mario Dudjak
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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
openaire +1 more source