Results 51 to 60 of about 219,349 (181)

Toward a Balanced Feature Space for the Deep Imbalanced Regression

IEEE Access, 2023
Regression with imbalanced data has been regarded as a more realistic scenario due to the difficulty of data acquisition and label annotations. However, it has not been extensively studied compared to the imbalanced classification.
Jangho Lee
doaj   +1 more source

Data reduction techniques for highly imbalanced medicare Big Data

Journal of Big Data
In the domain of Medicare insurance fraud detection, handling imbalanced Big Data and high dimensionality remains a significant challenge. This study assesses the combined efficacy of two data reduction techniques: Random Undersampling (RUS), and a novel
John T. Hancock, Huanjing Wang, Taghi M. Khoshgoftaar, Qianxin Liang   +3 more
doaj   +1 more source

Feature selection for classification using WGCNA and Spread Sub-Sample for an imbalanced rheumatoid arthritis RNASEQ data

Informatics in Medicine Unlocked, 2023
An imbalanced classification problem occurs when the distribution of samples among different classes is uneven or biased. Handling small and imbalanced training datasets poses a notable challenge in machine learning, especially in domains such as ...
Consolata Gakii, Victoria Mukami, Boaz Too   +2 more
doaj   +1 more source

Predictive Modeling of ICU Healthcare-Associated Infections from Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling Approach

, 2019
Early detection of patients vulnerable to infections acquired in the hospital environment is a challenge in current health systems given the impact that such infections have on patient mortality and healthcare costs.
Ballesteros-Herráez, Juan Carlos, Kraiem, Mohamed S., Moreno-García, María N., Sánchez-Barba, Mercedes, Sánchez-Hernández, Fernando   +4 more
core   +1 more source

Oversampling for Imbalanced Learning Based on K-Means and SMOTE

, 2017
Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced class distributions. While different strategies exist to tackle this problem,
Bacao, Fernando, Douzas, Georgios, Last, Felix   +2 more
core   +1 more source

Integrative machine learning approach for multi-class SCOP protein fold classification [PDF]

, 2003
Classification and prediction of protein structure has been a central research theme in structural bioinformatics. Due to the imbalanced distribution of proteins over multi SCOP classification, most discriminative machine learning suffers the well-known ‘
Deville, Y, Gilbert, D, Tan, A C
core  

Predictive Analytics Data Mining in Imbalanced Medical Dataset

Jurnal Komputer Terapan, 2016
Predictive Analytics Data Mining in Imbalanced Medical ...
Dini Hidayatul Qudsi
doaj  

Noise-Aware Undersampling for imbalanced medical data (NAUS)

Informatics in Medicine Unlocked
Advancements in medical research have increasingly relied on robust data analytics to support diagnostic and treatment decisions. However, data analysis still faces challenges when investigating datasets with severe class imbalance, often stemming from ...
Zholdas Buribayev, Ainur Yerkos, Zhibek Zhetpisbay, Markus Wolfien   +3 more
doaj   +1 more source

CLASSIFICATION BOOSTING IN IMBALANCED DATA

Malaysian Journal of Science, 2019
Most existing classification approaches assumed underlying training data set to be evenly distributed. However, in the imbalanced classification, the training data set of one majority class could far surpass those of the minority class. This becomes a problem because it’s usually produces biased classifiers that have a higher predictive accuracy over ...
Sinta Septi Pangastuti, Kartika Fithriasari, Nur Iriawan, Wahyuni Suryaningtyas   +3 more
openaire   +2 more sources

DPC-SMOTE Over-sampling Algorithm for Imbalanced Data Classification

Journal of Harbin University of Science and Technology
An oversampling algorithm based on density peak clustering is proposed to solve the problem of noise and imbalance among classes in imbalanced data sets.
LIU Zhihan, ZHANG Zhonglin, ZHAO Lei
doaj   +1 more source