Proceedings ArticleDOI
SVM classification: Optimization with the SMOTE algorithm for the class imbalance problem
Liliya Demidova,Irina Klyueva +1 more
- pp 1-4
TLDR
Experimental results show that the offered approach to selection of the optimal parameters values for the SMOTE (Synthetic Minority Over-sampling Technique) algorithm allows increasing the classification quality of the SVM classifier.Abstract:
In this paper a new approach to selection of the optimal parameters values for the SMOTE (Synthetic Minority Over-sampling Technique) algorithm in the problem of the SVM (Support Vector Machine) classification of imbalanced datasets has been suggested. This approach allows reducing the time expenditures for the search of the optimum parameters values of the SMOTE algorithm. The experimental results show that the offered approach allows increasing the classification quality of the SVM classifier.read more
Citations
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An Integrated Ensemble Learning Model for Imbalanced Fault Diagnostics and Prognostics
Zhenyu Wu,Wenfang Lin,Yang Ji +2 more
TL;DR: The feasibility and effectiveness of the proposed Easy-SMT method are validated, and it is shown that the model could also achieve good performance on multiclass imbalance learning task compared with baseline classifiers.
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A GAN-Based Anomaly Detection Approach for Imbalanced Industrial Time Series
TL;DR: This paper proposes a novel anomaly detection approach based on generative adversarial networks (GAN) to overcome the problem of class-imbalanced problems, where the number of normal samples is far larger than that of abnormal cases.
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A Weighted Deep Representation Learning Model for Imbalanced Fault Diagnosis in Cyber-Physical Systems.
TL;DR: A weighted Long Recurrent Convolutional LSTM model with sampling policy (wLRCL-D) to deal with imbalanced fault diagnosis for CPSs, which consists of 2-layer CNNs, 2- layer inner LSTMs and 2-Layer outer LSTm, with under-sampling policy and weighted cost-sensitive loss function.
Proceedings ArticleDOI
FakeTables: Using GANs to Generate Functional Dependency Preserving Tables with Bounded Real Data
TL;DR: This paper proposes a novel generative adversarial network framework called ITS-GAN, where both the generator and the discriminator are specifically designed to satisfy the functional dependencies of the released sub-table.
References
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Journal ArticleDOI
SMOTE: synthetic minority over-sampling technique
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
Journal ArticleDOI
SMOTE: Synthetic Minority Over-sampling Technique
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
BookDOI
Imbalanced Learning: Foundations, Algorithms, and Applications
Haibo He,Yunqian Ma +1 more
TL;DR: The first comprehensive look at this new branch of machine learning, this book offers a critical review of the problem of imbalanced learning, covering the state of the art in techniques, principles, and real-world applications.
Journal ArticleDOI
Handling class imbalance in customer churn prediction
Jonathan Burez,D Van den Poel +1 more
TL;DR: It is found that there is no need to under-sample so that there are as many churners in your training set as non churners, and under-sampling can lead to improved prediction accuracy, especially when evaluated with AUC.
Journal ArticleDOI
FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning
Rukshan Batuwita,Vasile Palade +1 more
TL;DR: A method to improve FSVMs for CIL (called FSVM-CIL), which can be used to handle the class imbalance problem in the presence of outliers and noise.
Related Papers (5)
Improving the Classification Quality of the SVM Classifier for the Imbalanced Datasets on the Base of Ideas the SMOTE Algorithm
Liliya Demidova,Irina Klyueva +1 more