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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   +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.
Gugulothu Narsimha, Kiran Kumar Bejjanki, Jayadev Gyani   +2 more
exaly   +2 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   +2 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 Hybrid Sampling Approach for Imbalanced Binary and Multi-Class Data Using Clustering Analysis

IEEE Access, 2022
Unequal data distribution among different classes usually cause a class imbalance problem. Due to the class imbalance, the classification models become biased toward the majority class and misclassify the minority class.
Abdul Sattar Palli, Jafreezal Jaafar, Manzoor Ahmed Hashmani, Heitor Murilo Gomes, Abdul Rehman Gilal   +4 more
doaj   +1 more source

Few-Shot Learning With Class Imbalance

IEEE Transactions on Artificial Intelligence, 2023
Few-Shot Learning (FSL) algorithms are commonly trained through Meta-Learning (ML), which exposes models to batches of tasks sampled from a meta-dataset to mimic tasks seen during evaluation. However, the standard training procedures overlook the real-world dynamics where classes commonly occur at different frequencies. While it is generally understood
Mateusz Ochal, Massimiliano Patacchiola, Jose Vazquez, Amos Storkey 0001, Sen Wang 0002   +4 more
openaire   +3 more sources

Class Uncertainty: A Measure to Mitigate Class Imbalance

CoRR, 2023
Class-wise characteristics of training examples affect the performance of deep classifiers. A well-studied example is when the number of training examples of classes follows a long-tailed distribution, a situation that is likely to yield sub-optimal performance for under-represented classes.
Zeynep Sonat Baltaci, Kemal Oksuz, Selim Kuzucu, Kivanc Tezoren, Berkin Kerim Konar, Alpay Ozkan, Emre Akbas, Sinan Kalkan   +7 more
openaire   +2 more sources

ADASYN-LOF Algorithm for Imbalanced Tornado Samples

Atmosphere, 2022
Early warning and forecasting of tornadoes began to combine artificial intelligence (AI) and machine learning (ML) algorithms to improve identification efficiency in the past few years.
Zhipeng Qing, Qiangyu Zeng, Hao Wang, Yin Liu, Taisong Xiong, Shihao Zhang   +5 more
doaj   +1 more source