Results 41 to 50 of about 103,585 (306)

A kernel-based two-class classifier for imbalanced data sets [PDF]

, 2007
Many kernel classifier construction algorithms adopt classification accuracy as performance metrics in model evaluation. Moreover, equal weighting is often applied to each data sample in parameter estimation.
Hong, X., Harris, C.J., Chen, S.
core   +1 more source

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

A Novel Imbalanced Ensemble Learning in Software Defect Predication

IEEE Access, 2021
With the availability of high-speed Internet and the advent of Internet of Things devices, modern software systems are growing in both size and complexity. Software defect prediction (SDP) guarantees the high quality of such complex systems. However, the
Jianming Zheng, Xingqi Wang, Dan Wei, Bin Chen, Yanli Shao   +4 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 ...
Titterington, D.M., Xue, J.H.
core   +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

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

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 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

Multi-output Regression for Imbalanced Data Stream

, 2023
International audienceIn this paper we describe an imbalanced regression method for making predictions over imbalanced data streams. We present MORSTS (Multiple Output Regression for Streaming Time Series), an online ensemble regressors devoted to ...
Sellami, Sana, Peng, Tao, Boucelma, Omar, Chbeir, Richard   +3 more
core   +1 more source