Academic Journal
A hierarchical and parallel branch-and-bound ensemble selection algorithm.
| Τίτλος: | A hierarchical and parallel branch-and-bound ensemble selection algorithm. |
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| Συγγραφείς: | Dai, Qun, Yao, ChangSheng |
| Πηγή: | Applied Intelligence; Jan2017, Vol. 46 Issue 1, p45-61, 17p |
| Θεματικοί όροι: | Algorithms, Mathematical optimization, Machine learning, Soft computing, Accuracy |
| Περίληψη: | This paper describes the development of an effective and efficient Hierarchical and Parallel Branch-and-Bound Ensemble Selection (H&PB&BEnS) algorithm. Using the proposed H&PB&BEnS, ensemble selection is accomplished in a divisional, parallel, and hierarchical way. H&PB&BEnS uses the superior performance of the Branch-and-Bound (B&B) algorithm in relation to small-scale combinational optimization problems, whilst also managing to avoid 'the curse of dimensionality' that can result from the direct application of B&B to ensemble selection problems. The B&B algorithm is used to select each partitioned subensemble, which enhances the predictive accuracy of each pruned subsolution, and then the working mechanism of H&PB&BEnS improves the diversity of the ensemble selection results. H&PB&BEnS realizes layer-wise refinement of the selected ensemble solutions, which enables the classification performance of the selected ensembles to be improved in a layer-by-layer manner. Empirical investigations are conducted using five benchmark classification datasets, and the results verify the effectiveness and efficiency of the proposed H&PB&BEnS algorithm. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Intelligence 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s10489-016-0817-8 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Items | – Name: Title Label: Title Group: Ti Data: A hierarchical and parallel branch-and-bound ensemble selection algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Dai%2C+Qun%22">Dai, Qun</searchLink><br /><searchLink fieldCode="AR" term="%22Yao%2C+ChangSheng%22">Yao, ChangSheng</searchLink> – Name: TitleSource Label: Source Group: Src Data: Applied Intelligence; Jan2017, Vol. 46 Issue 1, p45-61, 17p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Soft+computing%22">Soft computing</searchLink><br /><searchLink fieldCode="DE" term="%22Accuracy%22">Accuracy</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper describes the development of an effective and efficient Hierarchical and Parallel Branch-and-Bound Ensemble Selection (H&PB&BEnS) algorithm. Using the proposed H&PB&BEnS, ensemble selection is accomplished in a divisional, parallel, and hierarchical way. H&PB&BEnS uses the superior performance of the Branch-and-Bound (B&B) algorithm in relation to small-scale combinational optimization problems, whilst also managing to avoid 'the curse of dimensionality' that can result from the direct application of B&B to ensemble selection problems. The B&B algorithm is used to select each partitioned subensemble, which enhances the predictive accuracy of each pruned subsolution, and then the working mechanism of H&PB&BEnS improves the diversity of the ensemble selection results. H&PB&BEnS realizes layer-wise refinement of the selected ensemble solutions, which enables the classification performance of the selected ensembles to be improved in a layer-by-layer manner. Empirical investigations are conducted using five benchmark classification datasets, and the results verify the effectiveness and efficiency of the proposed H&PB&BEnS algorithm. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Applied Intelligence 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/s10489-016-0817-8 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 45 Subjects: – SubjectFull: Algorithms Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Soft computing Type: general – SubjectFull: Accuracy Type: general Titles: – TitleFull: A hierarchical and parallel branch-and-bound ensemble selection algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Dai, Qun – PersonEntity: Name: NameFull: Yao, ChangSheng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 0924669X Numbering: – Type: volume Value: 46 – Type: issue Value: 1 Titles: – TitleFull: Applied Intelligence Type: main |
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