A Review on Hybrid Artificial Bee Colony for Feature Selection

Simple item page
dc.contributor.authorJooda, Janet
dc.date.accessioned2022-05-04T08:10:01Z
dc.date.available2022-05-04T08:10:01Z
dc.date.issued2021-08-30
dc.description.abstractDue to the presence of redundant and irrelevant features in the dataset, the feature space's high dimensionality has an impact on classification accuracies and computational complexity. Feature Selection gets the most relevant and valuable information and aids in classification speed. Since finding the suitable, optimal feature subset is critical, feature selection is viewed as an optimization problem. One of the efficient nature-inspired optimization techniques for handling combinatorial optimization issues is the Artificial Bee Colony algorithm. It has no sensitive control parameters and has been demonstrated to compete with other well-known algorithms. However, it has a poor local search performance, with the equation of solution search in ABC performing well for exploration but poorly for exploitation. Furthermore, it has a quick convergence rate and can thus become caught in local optima for some complex multimodal situations. Since its introduction, much research has been conducted to address these issues in order to make ABC more efficient and applicable to a wide range of applications. This paper provides an overview of ABC advances, applications, comparative performance, and future research opportunities.en_US
dc.identifier.issn2394-5788
dc.identifier.urihttp://dspace.run.edu.ng:8080/jspui/handle/123456789/2593
dc.language.isoenen_US
dc.publisherGlobal Journal of Advanced Researchen_US
dc.relation.ispartofseriesVol-8, Issue-6;170-177
dc.subjectFeature selectionen_US
dc.subjectswarm intelligent algorithmsen_US
dc.subjectartificial bee colonyen_US
dc.subjectexplorationen_US
dc.subjectexploitationen_US
dc.subjectclassificationen_US
dc.titleA Review on Hybrid Artificial Bee Colony for Feature Selectionen_US
dc.typeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Jooda et al published paper.pdf
Size:
693.9 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Department of Computer Engineering