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A hybridization of multiple imputation and one-class bagging ensemble approach for missing value and class imbalance problem
Class imbalance in a dataset leads to erroneous outcomes that engrave the learning techniques and high misclassification cost in the minority class....
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Idecomp: imbalance-aware decomposition for class-decomposed classification using conditional GANs
Medical image classification tasks frequently encounter challenges associated with class imbalance, resulting in biased model training and suboptimal...
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Novel fuzzy clustering-based undersampling framework for class imbalance problem
The class imbalance problem occurs in various real-world datasets. Although it is considered that samples of the classes of a dataset are evenly...
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KSMOTEEN: A Cluster Based Hybrid Sampling Model for Imbalance Class Data
Classification accuracy for imbalance class data is a primary issue in machine learning. Most classification algorithms result in insignificant...
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Impact of class imbalance in VeReMi dataset for misbehavior detection in autonomous vehicles
Class imbalance is one of the common problems faced by the researchers in their learning or analyzing datasets. The learning precision and accuracy...
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Handling Class Imbalance Problem Using Support Vector Machine
The class imbalance problem makes it difficult to use a classification model. The model may not be trained appropriately due to the availability of a...
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Class imbalance data handling with optimal deep learning-based intrusion detection in IoT environment
The Internet of Things (IoT) has performed a paradigm shift in the method devices and systems interact, allowing seamless connectivity and...
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Class-specific extreme learning machine based on overall distribution for addressing binary imbalance problem
Class imbalance problem occurs when the training dataset contains significantly fewer samples of one class in contrast to another class. Conventional...
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Generative Models for Class Imbalance Problem on BreakHis Dataset: A Case Study
In real-world classification tasks, it is common to find class imbalance issues in the training datasets, i.e. an unequal number of examples among...
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VMD-Based Ensembled SMOTEBoost for Imbalanced Multi-class Rotor Mass Imbalance Fault Detection and Diagnosis Under Industrial Noise
PurposeThe purpose of this study is to investigate rotor imbalance fault diagnosis for multi-class imbalanced noisy data in rotor-bearing systems....
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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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Diversity based multi-cluster over sampling approach to alleviate the class imbalance problem in software defect prediction
Advancement in the field of Artificial Intelligence and Machine Learning has paved the way to enhance the quality of software by creating advanced...
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Diagnosis of Parkinson Disease Using Ensemble Methods for Class Imbalance Problem
Parkinson disease (PD) is the most prevalent degenerative neurological disorders that is incurable. Early PD diagnosis is essential in order to...
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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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Handling Class Imbalance Problem Using Feature Selection Techniques: A Review
The performance of traditional machine learning techniques may deteriorate while dealing with complex data due to biased distribution of samples,...
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Appraising Machine and Deep Learning Techniques for Traffic Conflict Prediction with Class Imbalance
Predicting traffic conflicts is pivotal for vehicle-based active safety system to prevent crashes. Yet, conflict prediction is a challenging task as...
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Transfer Learning-Based Class Imbalance-Aware Shoulder Implant Classification from X-Ray Images
Total shoulder arthroplasty is a standard restorative procedure practiced by orthopedists to diagnose shoulder arthritis in which a prosthesis...
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A Hybrid Evolutionary Under-sampling Method for Handling the Class Imbalance Problem with Overlap in Credit Classification
Credit risk assessment is an important task of risk management for financial institutions. Machine learning-based approaches have made promising...
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Using Deep Learning and Class Imbalance Techniques to Predict Software Defects
A software application defect is a variance or diversion from the end user's needs or the original business requirements. A software defect is a...
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Addressing Class Imbalance in Fake News Detection with Latent Space Resampling
The detection of fake news has become crucial with the popularity of social media as a primary medium of news consumption. However, real-world fake...