.png){kind=icon}
Return
Minimum class variance class-specific extreme learning machine for imbalanced classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2021.114994.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Imbalanced problems occur in real-world applications when the number of majority instances far exceeds the number of minority instances. Traditional extreme learning machine (ELM) classifier becomes biased towards the majority class due to imbalanced learning. To handle this inherent drawback, several modifications of ELM have been proposed such as weighted ELM (WELM), variances-constrained WELM (VW-ELM) to tackle the class imbalance problem effectively. One of our recent works class-specific ELM (CSELM) employs class-specific regularization and has been shown to outperform WELM for imbalanced learning. Motivated by CSELM, this work proposes a minimum class variance class-specific extreme learning machine (MCVCSELM), a variant of CSELM for tackling binary class imbalance problems more effectively. MCVCSELM uses the advantages of both the minimum class variance and the class-specific regularization. The proposed work also has lower computational complexity compared to WELM and VW-ELM. In class-specific cost regulation ELM (CCR-ELM), the calculation of the regularization parameters does not consider class distribution and class overlap. However, the performance of the CCR-ELM is comparable to ELM. MCVCSELM utilizes a class-specific regularization parameter whose value is decided by using the class proportion. The experimental results on 38 binary class datasets with different imbalanced ratios demonstrate that the proposed algorithm outperforms several state-of-the-art methods for imbalanced learning.
Keywords:
Extreme learning machine
Minimum class variance class-specific extreme learning machine
Class imbalance problem
Classification
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W
Organization
Cited Papers
Generalized class-specific kernelized extreme learning machine for multiclass imbalanced learning
PREAI
ShareSaveA systematic study of the class imbalance problem in convolutional neural networks.png)
NEURAL NETWORKS
OAAI
NEURAL NETWORKSIF6.3
OAAIShareSaveProductivity enhancement of solar still by PCM and Nanoparticles miscellaneous basin absorbing materialsDesalinationOAAI
DesalinationIF0
OAAIShareSaveShareSaveStochastic gradient based extreme learning machines for stable online learning of advanced combustion enginesNEUROCOMPUTINGOAAI
NEUROCOMPUTINGIF6.5
OAAIShareSaveArsenic removal from aqueous solutions by adsorption using novel MIL-53(Fe) as a highly efficient adsorbentRSC Advances
RSC AdvancesIF0
PREAI
ShareSaveOnline sequential class-specific extreme learning machine for binary imbalanced learningNEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
ShareSaveClass-specific cost-sensitive boosting weighted ELM for class imbalance learningMEMETIC COMPUTING
MEMETIC COMPUTINGIF2.3
PREAI
ShareSave
View more