Results 21 to 30 of about 224,004 (328)

A novel hybrid predictive maintenance model based on clustering, smote and multi-layer perceptron neural network optimised with grey wolf algorithm

SN Applied Sciences, 2021
Considering the complexities and challenges in the classification of multiclass and imbalanced fault conditions, this study explores the systematic combination of unsupervised and supervised learning by hybridising clustering (CLUST) and optimised multi ...
Albert Buabeng, Anthony Simons, Nana Kena Frempong, Yao Yevenyo Ziggah   +3 more
doaj   +1 more source

Single-Point Crossover and Jellyfish Optimization for Handling Imbalanced Data Classification Problem

IEEE Access, 2022
The imbalanced datasets and their classification has pulled in as a hot research topic over the years. It is used in different fields, for example, security, finance, health, and many others.
Abeer S. Desuky, Yomna M. Elbarawy, Samina Kausar, Asmaa Hekal Omar, Sadiq Hussain   +4 more
doaj   +1 more source

Imbalanced Data Classification Algorithm Based on CSD-ELM [PDF]

Jisuanji gongcheng, 2019
The Extreme Learning Machine(ELM) based on cost-sensitive learning has its advantages in dealing with imbalanced data classification problems.However,it fails to consider the distribution characteristics of samples in different classes and the importance
WANG Dafei, XIE Wujie, DONG Wenhan
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

Do unbalanced data have a negative effect on LDA? [PDF]

, 2008
For two-class discrimination, Xie and Qiu [The effect of imbalanced data sets on LDA: a theoretical and empirical analysis, Pattern Recognition 40 (2) (2007) 557–562] claimed that, when covariance matrices of the two classes were unequal, a (class ...
Anderson, Bradley, Chawla, D. Michael Titterington, Gilbert, Hanley, Jing-Hao Xue, Marks, McLachlan, Titterington, Weiss, Weiss, Xie   +12 more
core   +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

SMOTE-LOF for noise identification in imbalanced data classification

Journal of King Saud University: Computer and Information Sciences, 2022
Imbalanced data typically refers to a condition in which several data samples in a certain problem is not equally distributed, thereby leading to the underrepresentation of one or more classes in the dataset.
Asniar, Nur Ulfa Maulidevi, Kridanto Surendro   +2 more
doaj   +1 more source

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

Improved PSO_AdaBoost Ensemble Algorithm for Imbalanced Data

Sensors, 2019
The Adaptive Boosting (AdaBoost) algorithm is a widely used ensemble learning framework, and it can get good classification results on general datasets.
Kewen Li, Guangyue Zhou, Jiannan Zhai, Fulai Li, Mingwen Shao   +4 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