LabCor: Multi-label classification using a label correction strategy.
Saved in:
| Title: | LabCor: Multi-label classification using a label correction strategy. |
|---|---|
| Authors: | Wu, Chengkai1 (AUTHOR), Zhou, Tianshu2 (AUTHOR), Wu, Junya1 (AUTHOR), Tian, Yu1 (AUTHOR), Li, Jingsong1,2 (AUTHOR) ljs@zju.edu.cn |
| Source: | Applied Intelligence. Mar2022, Vol. 52 Issue 5, p5414-5434. 21p. |
| Subjects: | Classification, Machine learning |
| Abstract: | Multi-label classification is a branch of machine learning that can effectively reflect real-world problems. Among all the multi-label classification methods, stacked binary relevance (2BR) is a classic approach. Based on 2BR, a series of optimized algorithms have been derived. Although these algorithms have adapted complex optimizing processes and shown remarkable performance, their core concepts are rather similar, mainly involving restructuring the feature spaces of meta-classifiers. Existing research has rarely discussed that the use of inappropriate two-level predictions causes negative impacts on 2BR structures. In this study, we propose a 2BR-based label correction method named LabCor, which focuses on the identification and correction of unreliable two-level predictions. We first discuss the inner mechanism by which the 2BR-based algorithms obtain their two-level outputs and find a marker that can reflect the reliabilities of the samples predictions. Based on the mechanism, we then introduce a graph based output determination method that can use the training samples to generate dimensional decision patterns. The global label count distribution is also used to reflect the goal of classification problems. In the prediction phase, LabCor uses the decision patterns and label count constraints to identify and correct the misclassified labels. According to the evaluation results, the proposed method can effectively reduce the impact of troubling two-level predictions and yield superior or competitive performance versus well-established 2BR-based algorithms. [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.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 155626647 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: LabCor: Multi-label classification using a label correction strategy. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wu%2C+Chengkai%22">Wu, Chengkai</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Tianshu%22">Zhou, Tianshu</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Junya%22">Wu, Junya</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tian%2C+Yu%22">Tian, Yu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Jingsong%22">Li, Jingsong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> ljs@zju.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Intelligence%22">Applied Intelligence</searchLink>. Mar2022, Vol. 52 Issue 5, p5414-5434. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Multi-label classification is a branch of machine learning that can effectively reflect real-world problems. Among all the multi-label classification methods, stacked binary relevance (2BR) is a classic approach. Based on 2BR, a series of optimized algorithms have been derived. Although these algorithms have adapted complex optimizing processes and shown remarkable performance, their core concepts are rather similar, mainly involving restructuring the feature spaces of meta-classifiers. Existing research has rarely discussed that the use of inappropriate two-level predictions causes negative impacts on 2BR structures. In this study, we propose a 2BR-based label correction method named LabCor, which focuses on the identification and correction of unreliable two-level predictions. We first discuss the inner mechanism by which the 2BR-based algorithms obtain their two-level outputs and find a marker that can reflect the reliabilities of the samples predictions. Based on the mechanism, we then introduce a graph based output determination method that can use the training samples to generate dimensional decision patterns. The global label count distribution is also used to reflect the goal of classification problems. In the prediction phase, LabCor uses the decision patterns and label count constraints to identify and correct the misclassified labels. According to the evaluation results, the proposed method can effectively reduce the impact of troubling two-level predictions and yield superior or competitive performance versus well-established 2BR-based algorithms. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=155626647 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10489-021-02674-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 5414 Subjects: – SubjectFull: Classification Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: LabCor: Multi-label classification using a label correction strategy. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wu, Chengkai – PersonEntity: Name: NameFull: Zhou, Tianshu – PersonEntity: Name: NameFull: Wu, Junya – PersonEntity: Name: NameFull: Tian, Yu – PersonEntity: Name: NameFull: Li, Jingsong IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 03 Text: Mar2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 0924669X Numbering: – Type: volume Value: 52 – Type: issue Value: 5 Titles: – TitleFull: Applied Intelligence Type: main |
| ResultId | 1 |
