Multiple Filter-Based Rankers to Guide Hybrid Grasshopper Optimization Algorithm and Simulated Annealing for Feature Selection With High Dimensional Multi-Class Imbalanced Datasets

Multiple Filter-Based Rankers to Guide Hybrid Grasshopper Optimization Algorithm and Simulated Annealing for Feature Selection With High Dimensional Multi-Class Imbalanced Datasets

DNA microarray data analysis is infamous due to a massive number of features, imbalanced class distribution, and limited available samples. In this paper, we focus on high-dimensional multi-class imbalanced problems. The high dimensional and multi-class imbalanced problem has posed acute challenges...

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Bibliographic Details
Main Authors: Abdulrauf Garba Sharifai, Zurinahni Binti Zainol
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Grasshopper optimization algorithm
simulated annealing
tournament selection
multiple filter-based rankers
feature selection
hybrid filter-wrapper
Online Access:https://ieeexplore.ieee.org/document/9433565/

Internet

https://ieeexplore.ieee.org/document/9433565/

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