Cancer Classification through Gene Selection Using the Social Spider Optimization Algorithm.
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| Title: | Cancer Classification through Gene Selection Using the Social Spider Optimization Algorithm. |
|---|---|
| Authors: | Cherif, Chahira1 cherif.chahira@univ-oran1.dz, Maiza, Mohammed2 maiza.mohammed@univ-oran1.dz, Chouraqui, Samira3 samira.chouraqui@univ-usto.dz, Taleb-Ah, Abdelmalik abdelmalik.taleb-ahmed@uphf.fr |
| Source: | Informatica (03505596). Oct2025, Vol. 49 Issue 3, p537-550. 14p. |
| Subjects: | Microarray technology, Feature selection, Machine learning, Tumors, Data management, Swarm intelligence, Classification |
| Abstract: | Cancer is a leading cause of global mortality, underscoring the need for advanced diagnostic tools to enable early and accurate detection. Microarray technology allows for the simultaneous analysis of thousands of genes, offering valuable insights into cancer biology. However, the high dimensionality of microarray data presents significant challenges for classification tasks. In this study, we propose a novel approach that integrates the Social Spider Optimization (SSO) algorithm with mutual information-based feature selection to identify the most discriminative genes for cancer classification. We evaluate the performance of four machine learning classifiers—Decision Tree (DT), K-Nearest Neighbors (K-NN), Neural Networks (NN), and Support Vector Machines (SVM)—with and without feature selection. Our results demonstrate that the SSO algorithm significantly enhances classification accuracy, with SVM achieving near-perfect performance on leukemia and lymphoma datasets when combined with Max-Relevance Min-Redundancy (MRMR) feature selection. This hybrid approach provides a robust solution for cancer diagnosis by addressing key challenges such as data redundancy and computational complexity. [ABSTRACT FROM AUTHOR] |
| Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 188812673 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Cancer Classification through Gene Selection Using the Social Spider Optimization Algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cherif%2C+Chahira%22">Cherif, Chahira</searchLink><relatesTo>1</relatesTo><i> cherif.chahira@univ-oran1.dz</i><br /><searchLink fieldCode="AR" term="%22Maiza%2C+Mohammed%22">Maiza, Mohammed</searchLink><relatesTo>2</relatesTo><i> maiza.mohammed@univ-oran1.dz</i><br /><searchLink fieldCode="AR" term="%22Chouraqui%2C+Samira%22">Chouraqui, Samira</searchLink><relatesTo>3</relatesTo><i> samira.chouraqui@univ-usto.dz</i><br /><searchLink fieldCode="AR" term="%22Taleb-Ah%2C+Abdelmalik%22">Taleb-Ah, Abdelmalik</searchLink><i> abdelmalik.taleb-ahmed@uphf.fr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Informatica+%2803505596%29%22">Informatica (03505596)</searchLink>. Oct2025, Vol. 49 Issue 3, p537-550. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Microarray+technology%22">Microarray technology</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Tumors%22">Tumors</searchLink><br /><searchLink fieldCode="DE" term="%22Data+management%22">Data management</searchLink><br /><searchLink fieldCode="DE" term="%22Swarm+intelligence%22">Swarm intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Cancer is a leading cause of global mortality, underscoring the need for advanced diagnostic tools to enable early and accurate detection. Microarray technology allows for the simultaneous analysis of thousands of genes, offering valuable insights into cancer biology. However, the high dimensionality of microarray data presents significant challenges for classification tasks. In this study, we propose a novel approach that integrates the Social Spider Optimization (SSO) algorithm with mutual information-based feature selection to identify the most discriminative genes for cancer classification. We evaluate the performance of four machine learning classifiers—Decision Tree (DT), K-Nearest Neighbors (K-NN), Neural Networks (NN), and Support Vector Machines (SVM)—with and without feature selection. Our results demonstrate that the SSO algorithm significantly enhances classification accuracy, with SVM achieving near-perfect performance on leukemia and lymphoma datasets when combined with Max-Relevance Min-Redundancy (MRMR) feature selection. This hybrid approach provides a robust solution for cancer diagnosis by addressing key challenges such as data redundancy and computational complexity. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=188812673 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.31449/inf.v49i3.9126 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 537 Subjects: – SubjectFull: Microarray technology Type: general – SubjectFull: Feature selection Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Tumors Type: general – SubjectFull: Data management Type: general – SubjectFull: Swarm intelligence Type: general – SubjectFull: Classification Type: general Titles: – TitleFull: Cancer Classification through Gene Selection Using the Social Spider Optimization Algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cherif, Chahira – PersonEntity: Name: NameFull: Maiza, Mohammed – PersonEntity: Name: NameFull: Chouraqui, Samira – PersonEntity: Name: NameFull: Taleb-Ah, Abdelmalik IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 03505596 Numbering: – Type: volume Value: 49 – Type: issue Value: 3 Titles: – TitleFull: Informatica (03505596) Type: main |
| ResultId | 1 |
