Survey on deep learning with class imbalance
JM Johnson, TM Khoshgoftaar - Journal of big data, 2019 - Springer
The purpose of this study is to examine existing deep learning techniques for addressing
class imbalanced data. Effective classification with imbalanced data is an important area of …
class imbalanced data. Effective classification with imbalanced data is an important area of …
[HTML][HTML] A comprehensive data level analysis for cancer diagnosis on imbalanced data
The early diagnosis of cancer, as one of the major causes of death, is vital for cancerous
patients. Diagnosing diseases in general and cancer in particular is a considerable …
patients. Diagnosing diseases in general and cancer in particular is a considerable …
Slime mould algorithm: A new method for stochastic optimization
In this paper, a new stochastic optimizer, which is called slime mould algorithm (SMA), is
proposed based on the oscillation mode of slime mould in nature. The proposed SMA has …
proposed based on the oscillation mode of slime mould in nature. The proposed SMA has …
Neighborhood linear discriminant analysis
Abstract Linear Discriminant Analysis (LDA) assumes that all samples from the same class
are independently and identically distributed (iid). LDA may fail in the cases where the …
are independently and identically distributed (iid). LDA may fail in the cases where the …
Orthogonal learning covariance matrix for defects of grey wolf optimizer: Insights, balance, diversity, and feature selection
This research's genesis is in two aspects: first, a guaranteed solution for mitigating the grey
wolf optimizer's (GWO) defect and deficiencies. Second, we provide new open-minding …
wolf optimizer's (GWO) defect and deficiencies. Second, we provide new open-minding …
[图书][B] Learning from imbalanced data sets
Learning with imbalanced data refers to the scenario in which the amounts of instances that
represent the concepts in a given problem follow a different distribution. The main issue …
represent the concepts in a given problem follow a different distribution. The main issue …
Solving nonlinear equation systems based on evolutionary multitasking with neighborhood-based speciation differential evolution
Q Gu, S Li, Z Liao - Expert Systems with Applications, 2024 - Elsevier
Locating multiple roots of nonlinear equation systems (NESs) remains a challenging and
meaningful task in the numerical optimization community. Although a large number of NES …
meaningful task in the numerical optimization community. Although a large number of NES …
An evolutionary multitasking optimization framework for constrained multiobjective optimization problems
When addressing constrained multiobjective optimization problems (CMOPs) via
evolutionary algorithms, various constraints and multiple objectives need to be satisfied and …
evolutionary algorithms, various constraints and multiple objectives need to be satisfied and …
Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies
The first powerful variant of the Harris hawks optimization (HHO) is proposed in this work.
HHO is a recently developed swarm-based stochastic algorithm that has previously shown …
HHO is a recently developed swarm-based stochastic algorithm that has previously shown …
Enhanced Moth-flame optimizer with mutation strategy for global optimization
Y Xu, H Chen, J Luo, Q Zhang, S Jiao, X Zhang - Information Sciences, 2019 - Elsevier
Moth-flame optimization (MFO) is a widely used nature-inspired algorithm characterized by a
simple structure with simple parameters. However, for some complex optimization tasks …
simple structure with simple parameters. However, for some complex optimization tasks …