Results 51 to 60 of about 2,374,745 (297)

A Consolidated Decision Tree-Based Intrusion Detection System for Binary and Multiclass Imbalanced Datasets

Mathematics, 2021
The widespread acceptance and increase of the Internet and mobile technologies have revolutionized our existence. On the other hand, the world is witnessing and suffering due to technologically aided crime methods.
Ranjit Panigrahi, Samarjeet Borah, Akash Kumar Bhoi, Muhammad Fazal Ijaz, Moumita Pramanik, Y. Kumar, R. Jhaveri   +6 more
semanticscholar   +1 more source

Active Class Incremental Learning for Imbalanced Datasets [PDF]

, 2020
Accepted in IPCV workshop from ...
Eden Belouadah, Adrian Popescu 0001, Umang Aggarwal, Léo Saci   +3 more
openaire   +2 more sources

Binary classification for imbalanced datasets using a novel metric method

Egyptian Informatics Journal
This work proposes a kernel amplification method with non-stationary characteristics for binary classification of non-noisy imbalanced datasets. Our methodology features two key innovations, including that a derived non-stationary kernel construction ...
Jian Zheng, Shengye Wang, Huyong Yan, Haichao Sun   +3 more
doaj   +1 more source

Data oversampling and imbalanced datasets: an investigation of performance for machine learning and feature engineering

Journal of Big Data
The classification of imbalanced datasets is a prominent task in text mining and machine learning. The number of samples in each class is not uniformly distributed; one class contains a large number of samples while the other has a small number ...
Muhammad Mujahid, Erol Kına, F. Rustam, Mónica Gracia Villar, Eduardo Silva Alvarado, Isabel de la Torre Díez, Imran Ashraf   +6 more
semanticscholar   +1 more source

RDPVR: Random Data Partitioning with Voting Rule for Machine Learning from Class-Imbalanced Datasets

Electronics, 2022
Since most classifiers are biased toward the dominant class, class imbalance is a challenging problem in machine learning. The most popular approaches to solving this problem include oversampling minority examples and undersampling majority examples ...
Ahmad Hassanat, Ahmad S. Tarawneh, Samer Subhi Abed, G. Altarawneh, Malek Q. Alrashidi, Mansoor Alghamdi   +5 more
semanticscholar   +1 more source

Imbalanced Data Classification Method Based on LSSASMOTE

IEEE Access, 2023
Imbalanced data exist extensively in the real world, and the classification of imbalanced data is a hot topic in machine learning. In order to classify imbalanced data more effectively, an oversampling method named LSSASMOTE is proposed in this paper ...
Zhi Wang, Qicheng Liu
doaj   +1 more source

Posterior Re-calibration for Imbalanced Datasets

CoRR, 2020
Accepted to NeurIPS ...
Junjiao Tian, Yen-Cheng Liu, Nathaniel Glaser, Yen-Chang Hsu, Zsolt Kira   +4 more
openaire   +3 more sources

Handling Imbalanced Datasets for Robust Deep Neural Network-Based Fault Detection in Manufacturing Systems

Applied Sciences, 2021
Over the recent years, Industry 4.0 (I4.0) technologies such as the Industrial Internet of Things (IIoT), Artificial Intelligence (AI), and the presence of Industrial Big Data (IBD) have helped achieve intelligent Fault Detection (FD) in manufacturing ...
Jefkine Kafunah, Muhammad Intizar Ali, John G. Breslin   +2 more
doaj   +1 more source

Constrained Oversampling: An Oversampling Approach to Reduce Noise Generation in Imbalanced Datasets With Class Overlapping

IEEE Access, 2022
Imbalanced datasets are pervasive in classification tasks and would cause degradation of the performance of classifiers in predicting minority samples. Oversampling is effective in resolving the class imbalance problem.
Changhui Liu, Sun Jin, Donghong Wang, Zichao Luo, Jianbo Yu, Bing-Hai Zhou, Changlin Yang   +6 more
semanticscholar   +1 more source

Admixture Mapping Reveals Candidate Regions for Methotrexate Neurotoxicity Susceptibility: A Reducing Disparities in Acute Leukemia Consortium Report

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Neurotoxicity is a rare, often dose‐limiting adverse effect of methotrexate (MTX) therapy that disproportionally affects Latino children. Factors contributing to the observed disparity are not well understood. This study leveraged admixture mapping to identify genetic regions associated with MTX‐related neurotoxicity susceptibility ...
Rachel D. Harris, Priya B. Shetty, Olga A. Taylor, Elizabeth G. Atkinson, Melissa A. Richard, M. Monica Gramatges, Mark Zobeck, Sandi Pruitt, Amy Hughes, M. Brooke Bernhardt, Ashley Chavana, Van Huynh, Kathleen Ludwig, Laura Klesse, Kenneth Heym, Timothy Griffin, Rodrigo Erana, Juan Carlos Bernini, Han Chen, Bing Yu, Philip J. Lupo, Karen R. Rabin, Michael E. Scheurer, Austin L. Brown, The REDIAL Consortium   +24 more
wiley   +1 more source