A novel bio-inspired hybrid multi-filter wrapper gene selection method with ensemble classifier for microarray data.
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| Title: | A novel bio-inspired hybrid multi-filter wrapper gene selection method with ensemble classifier for microarray data. |
|---|---|
| Authors: | Nouri-Moghaddam, Babak1 (AUTHOR), Ghazanfari, Mehdi1 (AUTHOR) mehdi@iust.ac.ir, Fathian, Mohammad1 (AUTHOR) |
| Source: | Neural Computing & Applications. Jun2023, Vol. 35 Issue 16, p11531-11561. 31p. |
| Subjects: | Optimization algorithms, Wrappers, Microarray technology, Metric spaces, Swarm intelligence, Genes, Feature selection |
| Abstract: | Microarray technology is known as one of the most important tools for collecting DNA expression data. This technology allows researchers to investigate and examine types of diseases and their origins. However, microarray data are often associated with a small sample size, a significant number of genes, imbalanced data, etc., making classification models inefficient. Thus, a new hybrid solution based on a multi-filter and adaptive chaotic multi-objective forest optimization algorithm (AC-MOFOA) is presented to solve the gene selection problem and construct the Ensemble Classifier. In the proposed solution, a multi-filter model (i.e., ensemble filter) is proposed as preprocessing step to reduce the dataset's dimensions, using a combination of five filter methods to remove redundant and irrelevant genes. Accordingly, the results of the five filter methods are combined using a voting-based function. Additionally, the results of the proposed multi-filter indicate that it has good capability in reducing the gene subset size and selecting relevant genes. Then, an AC-MOFOA based on the concepts of non-dominated sorting, crowding distance, chaos theory, and adaptive operators is presented. AC-MOFOA as a wrapper method aimed at reducing dataset dimensions, optimizing KELM, and increasing the accuracy of the classification, simultaneously. Next, in this method, an ensemble classifier model is presented using AC-MOFOA results to classify microarray data. The performance of the proposed algorithm was evaluated on nine public microarray datasets, and its results were compared in terms of the number of selected genes, classification efficiency, execution time, time complexity, hypervolume indicator, and spacing metric with five hybrid multi-objective methods, and three hybrid single-objective methods. According to the results, the proposed hybrid method could increase the accuracy of the KELM in most datasets by reducing the dataset's dimensions and achieve similar or superior performance compared to other multi-objective methods. Furthermore, the proposed Ensemble Classifier model could provide better classification accuracy and generalizability in the seven of nine microarray datasets compared to conventional ensemble methods. Moreover, the comparison results of the Ensemble Classifier model with three state-of-the-art ensemble generation methods indicate its competitive performance in which the proposed ensemble model achieved better results in the five of nine datasets. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computing & Applications is the property of Springer Nature 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 163722426 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A novel bio-inspired hybrid multi-filter wrapper gene selection method with ensemble classifier for microarray data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nouri-Moghaddam%2C+Babak%22">Nouri-Moghaddam, Babak</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ghazanfari%2C+Mehdi%22">Ghazanfari, Mehdi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mehdi@iust.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Fathian%2C+Mohammad%22">Fathian, Mohammad</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Jun2023, Vol. 35 Issue 16, p11531-11561. 31p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Wrappers%22">Wrappers</searchLink><br /><searchLink fieldCode="DE" term="%22Microarray+technology%22">Microarray technology</searchLink><br /><searchLink fieldCode="DE" term="%22Metric+spaces%22">Metric spaces</searchLink><br /><searchLink fieldCode="DE" term="%22Swarm+intelligence%22">Swarm intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Genes%22">Genes</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Microarray technology is known as one of the most important tools for collecting DNA expression data. This technology allows researchers to investigate and examine types of diseases and their origins. However, microarray data are often associated with a small sample size, a significant number of genes, imbalanced data, etc., making classification models inefficient. Thus, a new hybrid solution based on a multi-filter and adaptive chaotic multi-objective forest optimization algorithm (AC-MOFOA) is presented to solve the gene selection problem and construct the Ensemble Classifier. In the proposed solution, a multi-filter model (i.e., ensemble filter) is proposed as preprocessing step to reduce the dataset's dimensions, using a combination of five filter methods to remove redundant and irrelevant genes. Accordingly, the results of the five filter methods are combined using a voting-based function. Additionally, the results of the proposed multi-filter indicate that it has good capability in reducing the gene subset size and selecting relevant genes. Then, an AC-MOFOA based on the concepts of non-dominated sorting, crowding distance, chaos theory, and adaptive operators is presented. AC-MOFOA as a wrapper method aimed at reducing dataset dimensions, optimizing KELM, and increasing the accuracy of the classification, simultaneously. Next, in this method, an ensemble classifier model is presented using AC-MOFOA results to classify microarray data. The performance of the proposed algorithm was evaluated on nine public microarray datasets, and its results were compared in terms of the number of selected genes, classification efficiency, execution time, time complexity, hypervolume indicator, and spacing metric with five hybrid multi-objective methods, and three hybrid single-objective methods. According to the results, the proposed hybrid method could increase the accuracy of the KELM in most datasets by reducing the dataset's dimensions and achieve similar or superior performance compared to other multi-objective methods. Furthermore, the proposed Ensemble Classifier model could provide better classification accuracy and generalizability in the seven of nine microarray datasets compared to conventional ensemble methods. Moreover, the comparison results of the Ensemble Classifier model with three state-of-the-art ensemble generation methods indicate its competitive performance in which the proposed ensemble model achieved better results in the five of nine datasets. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00521-021-06459-9 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 31 StartPage: 11531 Subjects: – SubjectFull: Optimization algorithms Type: general – SubjectFull: Wrappers Type: general – SubjectFull: Microarray technology Type: general – SubjectFull: Metric spaces Type: general – SubjectFull: Swarm intelligence Type: general – SubjectFull: Genes Type: general – SubjectFull: Feature selection Type: general Titles: – TitleFull: A novel bio-inspired hybrid multi-filter wrapper gene selection method with ensemble classifier for microarray data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nouri-Moghaddam, Babak – PersonEntity: Name: NameFull: Ghazanfari, Mehdi – PersonEntity: Name: NameFull: Fathian, Mohammad IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 35 – Type: issue Value: 16 Titles: – TitleFull: Neural Computing & Applications Type: main |
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