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HS-Gen: a hypersphere-constrained generation mechanism to improve synthetic minority oversampling for imbalanced classification
Mitigating the impact of class-imbalance data on classifiers is a challenging task in machine learning. SMOTE is a well-known method to tackle this...
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SMMO-CoFS: Synthetic Multi-minority Oversampling with Collaborative Feature Selection for Network Intrusion Detection System
Researchers publish various studies to improve the performance of network intrusion detection systems. However, there is still a high false alarm...
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OBMI: oversampling borderline minority instances by a two-stage Tomek link-finding procedure for class imbalance problem
Mitigating the impact of class imbalance datasets on classifiers poses a challenge to the machine learning community. Conventional classifiers do not...
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ASN-SMOTE: a synthetic minority oversampling method with adaptive qualified synthesizer selection
Oversampling is a promising preprocessing technique for imbalanced datasets which generates new minority instances to balance the dataset. However,...
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Importance-SMOTE: a synthetic minority oversampling method for noisy imbalanced data
Synthetic minority oversampling methods have been proven to be an efficient solution for tackling imbalanced data classification issues. Different...
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Class-biased sarcasm detection using BiLSTM variational autoencoder-based synthetic oversampling
Recent research works have established the importance of sarcasm detection in the domain of sentiment analysis. Automatic sarcasm detection using...
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Comparative analysis of machine learning and ensemble approaches for hepatitis B prediction using data mining with synthetic minority oversampling technique
PurposeHepatitis B, caused by the Hepatitis B virus (HBV), can harm the liver without noticeable symptoms. Early detection is crucial to prevent...
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Landslide Classification Using Deep Convolutional Neural Network with Synthetic Minority Oversampling Technique
Landslides are one of the world’s most devastating and catastrophic natural disasters affecting human life and the economy. Many machine...
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Global-local information based oversampling for multi-class imbalanced data
Multi-class imbalanced classification is a challenging problem in the field of machine learning. Many methods have been proposed to deal with it, and...
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Experimental Analysis of Oversampling Techniques in Class Imbalance Problem
The abstract should summarize the contents of the paper and should Class Imbalance is consistently being faced by real-world datasets, where one...
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Hierarchical Clustering-Based Synthetic Minority Data Generation for Handling Imbalanced Dataset
Predictive modeling is a new area of data science and machine learning that is gaining popularity. It provides sustained business growth, accurate...
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Perturbation-based oversampling technique for imbalanced classification problems
We present a simple yet effective idea, perturbation-based oversampling (POS), to tackle imbalanced classification problems. In this method, we...
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Investigating the Stability of SMOTE-Based Oversampling on COVID-19 Data
Predictive analytic methods for medical diagnosis can be helpful in supporting decision-making of medical treatment, which in turn reduce the need...
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Combination of Oversampling and Undersampling Techniques on Imbalanced Datasets
Many practical classification datasets are unbalanced, meaning that one of the classes is in the majority when compared to the others. In various...
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An oversampling algorithm of multi-label data based on cluster-specific samples and fuzzy rough set theory
Imbalanced class distributions are common in real-world scenarios, including datasets with multiple labels. One widely acknowledged approach to...
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Empirical Review of Oversampling Methods to Handle the Class Imbalance Problem
Many real-world applications, including fault detection and medical diagnostics, suffer from the Class Imbalance Problem (CIP), in which one class...
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Imbalanced fault classification of rolling bearing based on an improved oversampling method
Many works of bearing fault diagnosis based on vibration signals have been present. However, most of them work under ideal conditions that the fault...
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A cross-validation framework to find a better state than the balanced one for oversampling in imbalanced classification
Imbalance classification has always been a popular research point in the application of machine learning, data mining and pattern recognition. At...
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An Adaptive Oversampling Method for Imbalanced Datasets Based on Mean-Shift and SMOTE
Class imbalance is a challenge in different actual datasets, where the majority class contains a large number of data points, and the minority class...
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Genetic-Novelty Oversampling Technique for Imbalanced Data
Imbalance data is in important topic vexed researchers in practice of classification problems. A data is imbalanced if the distributions of...