Results 11 to 20 of about 4,590,312 (339)

Measuring the class-imbalance extent of multi-class problems [PDF]

Pattern Recognition Letters, 2017
TIN2013-41272P, IT609-13, AP2008 ...
Jonathan Ortigosa-Hernández, Iñaki Inza, José Antonio Lozano 0001   +2 more
openaire   +3 more sources

Addressing Class Imbalance in Federated Learning

Proceedings of the AAAI Conference on Artificial Intelligence, 2021
Federated learning (FL) is a promising approach for training decentralized data located on local client devices while improving efficiency and privacy. However, the distribution and quantity of the training data on the clients' side may lead to significant challenges such as class imbalance and non-IID (non-independent and identically distributed) data,
Lixu Wang, Shichao Xu, Xiao Wang 0012, Qi Zhu 0002   +3 more
openaire   +3 more sources

Analysis of classification metric behaviour under class imbalance

Egyptian Informatics Journal
Class imbalance is the phenomenon defined as skewed target variable distributions in a dataset. In other words class imbalance occurs when a dataset has an unequal proportion of target variables assigned to the instances in the dataset.
Jean-Pierre van Zyl, Andries Petrus Engelbrecht   +1 more
doaj   +2 more sources

Ensemble-SMOTE: Mitigating Class Imbalance in Graduate on Time Detection

Journal of Informatics and Web Engineering
In education, detecting students graduating on time is difficult due to high data complexity. Researchers have employed various approaches in identifying on-time graduation with Machine Learning, but it remains a challenging task due to the class ...
Theng-Jia Law, Choo-Yee Ting, Hu Ng, Hui-Ngo Goh, Albert Quek   +4 more
doaj   +2 more sources

A survey on addressing high-class imbalance in big data

Journal of Big Data, 2018
In a majority–minority classification problem, class imbalance in the dataset(s) can dramatically skew the performance of classifiers, introducing a prediction bias for the majority class.
Joffrey L. Leevy, Taghi M. Khoshgoftaar, Richard A. Bauder, Naeem Seliya   +3 more
doaj   +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

The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression [PDF]

J. Am. Medical Informatics Assoc., 2022
Objective Methods to correct class imbalance (imbalance between the frequency of outcome events and nonevents) are receiving increasing interest for developing prediction models.
Ruben van den Goorbergh, M. Smeden, D. Timmerman, B. Calster   +3 more
semanticscholar   +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

FRAME: Feature Rectification for Class Imbalance Learning

IEEE Transactions on Knowledge and Data Engineering
Class imbalance learning is a challenging task in machine learning applications. To balance training data, traditional class imbalance learning approaches, such as class resampling or reweighting, are commonly applied in the literature. However, these methods can have significant limitations, particularly in the presence of noisy data, missing values ...
Xu Cheng 0003, Fan Shi 0001, Yao Zhang 0021, Huan Li 0003, Xiufeng Liu 0001, Shengyong Chen   +5 more
openaire   +4 more sources

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