Cargando…
An Innovative Excited-ACS-IDGWO Algorithm for Optimal Biomedical Data Feature Selection
Finding an optimal set of discriminative features is still a crucial but challenging task in biomedical science. The complexity of the task is intensified when any of the two scenarios arise: a highly dimensioned dataset and a small sample-sized dataset. The first scenario poses a big challenge to e...
Autores principales: | Segera, Davies, Mbuthia, Mwangi, Nyete, Abraham |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7450338/ https://www.ncbi.nlm.nih.gov/pubmed/32908920 http://dx.doi.org/10.1155/2020/8506365 |
Ejemplares similares
-
Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis
por: Segera, Davies, et al.
Publicado: (2019) -
A Master-Slave Binary Grey Wolf Optimizer for Optimal Feature Selection in Biomedical Data Classification
por: Momanyi, Enock, et al.
Publicado: (2021) -
Hybrid Gradient Descent Grey Wolf Optimizer for Optimal Feature Selection
por: Kitonyi, Peter Mule, et al.
Publicado: (2021) -
An Aggressive Cuckoo Search Algorithm for Optimum Power Allocation in a CDMA-Based Cellular Network
por: Mwitia, Shawn Muthomi, et al.
Publicado: (2022) -
Feature Selection for High-Dimensional and Imbalanced Biomedical Data Based on Robust Correlation Based Redundancy and Binary Grasshopper Optimization Algorithm
por: Abdulrauf Sharifai, Garba, et al.
Publicado: (2020)