Building a training dataset for classification under a cost limitation.
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| Title: | Building a training dataset for classification under a cost limitation. |
|---|---|
| Authors: | Chen, Yen-Liang1 (AUTHOR) ylchen@mgt.ncu.edu.tw, Cheng, Li-Chen2 (AUTHOR) lijen.cheng@gmail.com, Zhang, Yi-Jun1 (AUTHOR) valorelove@gmail.com |
| Source: | Electronic Library. 2021, Vol. 39 Issue 1, p77-96. 20p. |
| Subject Terms: | *Cataloging, *Machine learning, *Algorithms, Conceptual structures, Research funding, Data analytics, Data mining |
| Abstract: | Purpose: A necessary preprocessing of document classification is to label some documents so that a classifier can be built based on which the remaining documents can be classified. Because each document differs in length and complexity, the cost of labeling each document is different. The purpose of this paper is to consider how to select a subset of documents for labeling with a limited budget so that the total cost of the spending does not exceed the budget limit, while at the same time building a classifier with the best classification results. Design/methodology/approach: In this paper, a framework is proposed to select the instances for labeling that integrate two clustering algorithms and two centroid selection methods. From the selected and labeled instances, five different classifiers were constructed with good classification accuracy to prove the superiority of the selected instances. Findings: Experimental results show that this method can establish a training data set containing the most suitable data under the premise of considering the cost constraints. The data set considers both "data representativeness" and "data selection cost," so that the training data labeled by experts can effectively establish a classifier with high accuracy. Originality/value: No previous research has considered how to establish a training set with a cost limit when each document has a distinct labeling cost. This paper is the first attempt to resolve this issue. [ABSTRACT FROM AUTHOR] |
| Copyright of Electronic Library is the property of Emerald Publishing Limited 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: | Education Research Complete |
| FullText | Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 150406194 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Building a training dataset for classification under a cost limitation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chen%2C+Yen-Liang%22">Chen, Yen-Liang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ylchen@mgt.ncu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Cheng%2C+Li-Chen%22">Cheng, Li-Chen</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> lijen.cheng@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yi-Jun%22">Zhang, Yi-Jun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> valorelove@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Electronic+Library%22">Electronic Library</searchLink>. 2021, Vol. 39 Issue 1, p77-96. 20p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Cataloging%22">Cataloging</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Conceptual+structures%22">Conceptual structures</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analytics%22">Data analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: A necessary preprocessing of document classification is to label some documents so that a classifier can be built based on which the remaining documents can be classified. Because each document differs in length and complexity, the cost of labeling each document is different. The purpose of this paper is to consider how to select a subset of documents for labeling with a limited budget so that the total cost of the spending does not exceed the budget limit, while at the same time building a classifier with the best classification results. Design/methodology/approach: In this paper, a framework is proposed to select the instances for labeling that integrate two clustering algorithms and two centroid selection methods. From the selected and labeled instances, five different classifiers were constructed with good classification accuracy to prove the superiority of the selected instances. Findings: Experimental results show that this method can establish a training data set containing the most suitable data under the premise of considering the cost constraints. The data set considers both "data representativeness" and "data selection cost," so that the training data labeled by experts can effectively establish a classifier with high accuracy. Originality/value: No previous research has considered how to establish a training set with a cost limit when each document has a distinct labeling cost. This paper is the first attempt to resolve this issue. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Electronic Library is the property of Emerald Publishing Limited 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: 10.1108/EL-07-2020-0209 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 77 Subjects: – SubjectFull: Cataloging Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Conceptual structures Type: general – SubjectFull: Research funding Type: general – SubjectFull: Data analytics Type: general – SubjectFull: Data mining Type: general Titles: – TitleFull: Building a training dataset for classification under a cost limitation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chen, Yen-Liang – PersonEntity: Name: NameFull: Cheng, Li-Chen – PersonEntity: Name: NameFull: Zhang, Yi-Jun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 02640473 Numbering: – Type: volume Value: 39 – Type: issue Value: 1 Titles: – TitleFull: Electronic Library Type: main |
| ResultId | 1 |
