Results 31 to 40 of about 90,025 (264)

Severely imbalanced Big Data challenges: investigating data sampling approaches

Journal of Big Data, 2019
Severe class imbalance between majority and minority classes in Big Data can bias the predictive performance of Machine Learning algorithms toward the majority (negative) class.
Tawfiq Hasanin, Taghi M. Khoshgoftaar, Joffrey L. Leevy, Richard A. Bauder   +3 more
doaj   +1 more source

Imbalanced Learning Based on Data-Partition and SMOTE

Information, 2018
Classification of data with imbalanced class distribution has encountered a significant drawback by most conventional classification learning methods which assume a relatively balanced class distribution. This paper proposes a novel classification method
Huaping Guo, Jun Zhou, Chang-An Wu
doaj   +1 more source

TGT: A Novel Adversarial Guided Oversampling Technique for Handling Imbalanced Datasets

Egyptian Informatics Journal, 2021
With the volume of data increasing exponentially, there is a growing interest in helping people to benefit from their data regardless of its poor quality.
Ayat Mahmoud, Ayman El-Kilany, Farid Ali, Sherif Mazen   +3 more
doaj   +1 more source

Reacting Imbalanced Data via Ensemble Learning Techniques [PDF]

IJCI International Journal of Computers and Information
In machine learning, dealing with imbalanced datasets remains a significant challenge. Class imbalance arises when the distribution of instances across classes is uneven, which can occur in both binary and multiclass problems with varying imbalance ...
Fatma Kindeel, Hatem Abdel-Kader, Amgad Elsayed   +2 more
doaj   +1 more source

Oversampling Algorithm Oriented to Subdivision of Minority Class in Imbalanced Data Set [PDF]

Jisuanji gongcheng, 2017
The distributions of the minority class samples in the imbalanced data set are discrepant.Traditional oversampling algorithms do not dispose this discrepancy.In order to handle this discrepancy,this paper proposes an oversampling algorithm oriented to ...
GU Ping,YANG Yang
doaj   +1 more source

Imbalanced data classification using MapReduce and relief

Journal of Information and Telecommunication, 2018
Classification of imbalanced data has been reported to require modification of standard classification algorithms and lately has attracted a lot of attention due to practical applications in industry, banking and finance.
Joanna Jedrzejowicz, Robert Kostrzewski, Jakub Neumann, Magdalena Zakrzewska   +3 more
doaj   +1 more source

Multi-class Boosting for Imbalanced Data [PDF]

, 2015
We consider the problem of multi-class classification with imbalanced data-sets. To this end, we introduce a cost-sensitive multi-class Boosting algorithm (BAdaCost) based on a generalization of the Boosting margin, termed multi-class cost-sensitive margin.
Fernández Baldera, Antonio, Buenaposada Biencinto, José Miguel, Baumela Molina, Luis   +2 more
openaire   +2 more sources

A Recapitulation of Imbalanced Data

International Journal of Innovative Technology and Exploring Engineering, 2020
In today’s authentic universe almost all applications are imbalanced. Data imbalance is growing faster than ever before as many systems are interested in extracting knowledge from lake of data. Imbalance issue occurs because required data is very rare and using that rare data if classification is done we may lead to inaccurate result.
Shaheen Layaq*, Dr. B. Manjula
openaire   +1 more source

Clinical, Histopathological, and Molecular Characterization of Pediatric MN1::ZNF341‐Associated Cancer

Pediatric Blood &Cancer, EarlyView.
ABSTRACT A lethal round‐cell malignancy with an MN1::ZNF341 fusion has recently been reported in three infants. Here, we describe four further tumors, three in newborns (including monozygotic twins), and one in an adolescent. Detailed clinical, radiological, and histopathological data differentiate these tumors from their main mimics, neuroblastoma and
Thomas R. W. Oliver, Dyanne Rampling, Barbara Walkowiak, Nathaniel D. Anderson, Manas Dave, Henry Lee‐Six, Sadie Baskind, Pooja Balasubramanian, Glenn Anderson, Conor Parks, Toochi Ogbonnah, Emilia R. Robertson, Simon Hannam, Rohan Verma, John C. Achermann, Liina Palm, Neil Sebire, Thomas Jacques, Tom Watson, Carryl Dryden, Karin Straathof, Mette Jorgensen, Olga Slater, Sam Behjati, Sarita Depani, J. Ciaran Hutchinson   +25 more
wiley   +1 more source

Processing imbalanced medical data at the data level with assisted-reproduction data as an example

BioData Mining
Objective Data imbalance is a pervasive issue in medical data mining, often leading to biased and unreliable predictive models. This study aims to address the urgent need for effective strategies to mitigate the impact of data imbalance on classification
Junliang Zhu, Shaowei Pu, Jiaji He, Dongchao Su, Weijie Cai, Xueying Xu, Hongbo Liu   +6 more
doaj   +1 more source