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Survey on deep learning with class imbalance [PDF]

Journal of Big Data, 2019
The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in many real ...
Justin M. Johnson, Taghi M. Khoshgoftaar
doaj   +4 more sources

Navigating extreme class imbalance in suicide risk prediction [PDF]

Frontiers in Psychiatry
BackgroundThe implementation of suicide risk models is challenging because the conditions in which they are developed often do not reflect those in which they are being used.
Christopher Kitchen, Anas Belouali, Paul S. Nestadt, Paul S. Nestadt, Holly C. Wilcox, Holly C. Wilcox, Hadi Kharrazi, Hadi Kharrazi   +7 more
doaj   +2 more sources

A Class Imbalance Loss for Imbalanced Object Recognition [PDF]

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
The class imbalance problem exists widely in vision data. In these imbalanced datasets, the majority classes dominate the loss and influence the gradient.
Linbin Zhang, Caiguang Zhang, Sinong Quan, Huaxin Xiao, Gangyao Kuang, Li Liu   +5 more
doaj   +3 more sources

Optimizing Class Imbalance in Facial Expression Recognition Using Dynamic Intra-Class Clustering [PDF]

Biomimetics
While deep neural networks demonstrate robust performance in visual tasks, the long-tail distribution of real-world data leads to significant recognition accuracy degradation in critical scenarios such as medical human–robot affective interaction ...
Qingdu Li, Keting Fu, Jian Liu, Yishan Li, Qinze Ren, Kang Xu, Junxiu Fu, Na Liu, Ye Yuan   +8 more
doaj   +2 more sources

Effects of Class Imbalance Countermeasures on Interpretability

IEEE Access
The widespread use of artificial intelligence (AI) in more and more real-world applications is accompanied by challenges that are not obvious at first glance. In machine learning, class imbalance, characterized by an imbalance in the frequency of classes,
David Cemernek, Shafaq Siddiqi, Roman Kern   +2 more
doaj   +2 more sources

Class Imbalance Reduction (CIR): A Novel Approach to Software Defect Prediction in the Presence of Class Imbalance

Symmetry, 2020
Software defect prediction (SDP) is the technique used to predict the occurrences of defects in the early stages of software development process. Early prediction of defects will reduce the overall cost of software and also increase its reliability. Most of the defect prediction methods proposed in the literature suffer from the class imbalance problem.
Kiran Kumar Bejjanki, Jayadev Gyani
exaly   +3 more sources

Federated Learning with Class Imbalance Reduction [PDF]

2021 29th European Signal Processing Conference (EUSIPCO), 2021
Federated learning (FL) is a promising technique that enables a large amount of edge computing devices to collaboratively train a global learning model. Due to privacy concerns, the raw data on devices could not be available for centralized server. Constrained by the spectrum limitation and computation capacity, only a subset of devices can be engaged ...
Miao Yang, Akitanoshou Wong, Hongbin Zhu, Haifeng Wang, Hua Qian   +4 more
openaire   +3 more sources

DATA IMBALANCE IN LANDSLIDE SUSCEPTIBILITY ZONATION: UNDER-SAMPLING FOR CLASS-IMBALANCE LEARNING [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020
Machine learning methods such as artificial neural network, support vector machine etc. require a large amount of training data, however, the number of landslide occurrences are limited in a study area.
S. K. Gupta, M. Jhunjhunwalla, A. Bhardwaj, D. P. Shukla   +3 more
doaj   +1 more source

Combining Hybrid Approach Redefinition-Multiclass Imbalance (HAR-MI) and Hybrid Sampling in Handling Multi-Class Imbalance and Overlapping

JOIV: International Journal on Informatics Visualization, 2021
The class imbalance problem in the multi-class dataset is more challenging to manage than the problem in the two classes and this problem is more complicated if accompanied by overlapping.
Hartono Hartono, Erianto Ongko
doaj   +1 more source

A novel generative adversarial networks modelling for the class imbalance problem in high dimensional omics data [PDF]

BMC Medical Informatics and Decision Making
Class imbalance remains a large problem in high-throughput omics analyses, causing bias towards the over-represented class when training machine learning-based classifiers.
Samuel Cusworth, Georgios V. Gkoutos, Animesh Acharjee   +2 more
doaj   +2 more sources