Search
Search Results
-
Imbalance factor: a simple new scale for measuring inter-class imbalance extent in classification problems
Learning from datasets that suffer from differences in absolute frequency of classes is one of the most challenging tasks in the machine learning...
-
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....
-
Low-shot learning and class imbalance: a survey
The tasks of few-shot, one-shot, and zero-shot learning—or collectively “low-shot learning” (LSL)—at first glance are quite similar to the...
-
A Data Augmentation Methodology to Reduce the Class Imbalance in Histopathology Images
Deep learning techniques have recently yielded remarkable results across various fields. However, the quality of these results depends heavily on the...
-
Tackling class imbalance in computer vision: a contemporary review
Class imbalance is a key issue affecting the performance of computer vision applications such as medical image analysis, objection detection and...
-
A systematic review for class-imbalance in semi-supervised learning
This review aims to examine the state of the art of semi-supervised learning (SSL) techniques for addressing class imbalanced data. Class imbalance...
-
An Experimental Study of the Joint Effects of Class Imbalance and Class Overlap
It has been pointed out that the class imbalance problem is one of the critical areas in classification. Furthermore, existing literatures show that...
-
Ensemble framework for concept drift detection and class imbalance in data streams
Many data mining application generate data in the form of streams called as streaming data and they arrive continuously. The distribution of data...
-
Finding the Proverbial Needle: Improving Minority Class Identification Under Extreme Class Imbalance
Imbalanced learning problems typically consist of data with skewed class distributions, coupled with large misclassification costs for the rare...
-
Exploring uplift modeling with high class imbalance
Uplift modeling refers to individual level causal inference. Existing research on the topic ignores one prevalent and important aspect: high class...
-
The class imbalance problem in deep learning
Deep learning has recently unleashed the ability for Machine learning (ML) to make unparalleled strides. It did so by confronting and successfully...
-
A literature survey on various aspect of class imbalance problem in data mining
Data has become much widely available in recent years. Since the past years, Learning classifiers from unbalanced data is a crucial issue that comes...
-
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...
-
Exploiting domain knowledge to address class imbalance and a heterogeneous feature space in multi-class classification
Real-world data of multi-class classification tasks often show complex data characteristics that lead to a reduced classification performance. Major...
-
An adaptive Bagging algorithm based on lightweight transformer for multi-class imbalance recognition
The class imbalance is a significant issue in machine learning, particularly in the context of multi-class imbalance. The current multi-class...
-
Binary classification with fuzzy logistic regression under class imbalance and complete separation in clinical studies
BackgroundIn binary classification for clinical studies, an imbalanced distribution of cases to classes and an extreme association level between the...
-
Fuzzy twin support vector machine based on affinity and class probability for class imbalance learning
Recently a robust and efficient classifier termed affinity and class probability-based fuzzy support vector machine (ACFSVM) was proposed to address...
-
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...
-
DBOS_US: a density-based graph under-sampling method to handle class imbalance and class overlap issues in software fault prediction
Improving software quality by predicting faults during the early stages of software development is a primary goal of software fault prediction (SFP)....
-
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...