Dynamic feature scaling for online learning of binary classifiers.
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| Title: | Dynamic feature scaling for online learning of binary classifiers. |
|---|---|
| Authors: | Bollegala, Danushka1 danushka.bollegala@liverpool.ac.uk |
| Source: | Knowledge-Based Systems. Aug2017, Vol. 129, p97-105. 9p. |
| Subjects: | Distance education, Telecourses, Online education, Machine learning, Artificial intelligence, Comprehension |
| Abstract: | Scaling feature values is an important step in numerous machine learning tasks. Different features can have different value ranges and some form of a feature scaling is often required in order to learn an accurate classifier. However, feature scaling is conducted as a preprocessing task prior to learning. This is problematic in an online setting because of two reasons. First, it might not be possible to accurately determine the value range of a feature at the initial stages of learning when we have observed only a handful of training instances. Second, the distribution of data can change over time, which render obsolete any feature scaling that we perform in a pre-processing step. We propose a simple but an effective method to dynamically scale features at train time, thereby quickly adapting to any changes in the data stream. We compare the proposed dynamic feature scaling method against more complex methods for estimating scaling parameters using several benchmark datasets for classification. Our proposed feature scaling method consistently outperforms more complex methods on all of the benchmark datasets and improves classification accuracy of a state-of-the-art online classification algorithm. [ABSTRACT FROM AUTHOR] |
| Copyright of Knowledge-Based Systems is the property of Elsevier B.V. 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 123371874 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Dynamic feature scaling for online learning of binary classifiers. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bollegala%2C+Danushka%22">Bollegala, Danushka</searchLink><relatesTo>1</relatesTo><i> danushka.bollegala@liverpool.ac.uk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Knowledge-Based+Systems%22">Knowledge-Based Systems</searchLink>. Aug2017, Vol. 129, p97-105. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Distance+education%22">Distance education</searchLink><br /><searchLink fieldCode="DE" term="%22Telecourses%22">Telecourses</searchLink><br /><searchLink fieldCode="DE" term="%22Online+education%22">Online education</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Comprehension%22">Comprehension</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Scaling feature values is an important step in numerous machine learning tasks. Different features can have different value ranges and some form of a feature scaling is often required in order to learn an accurate classifier. However, feature scaling is conducted as a preprocessing task prior to learning. This is problematic in an online setting because of two reasons. First, it might not be possible to accurately determine the value range of a feature at the initial stages of learning when we have observed only a handful of training instances. Second, the distribution of data can change over time, which render obsolete any feature scaling that we perform in a pre-processing step. We propose a simple but an effective method to dynamically scale features at train time, thereby quickly adapting to any changes in the data stream. We compare the proposed dynamic feature scaling method against more complex methods for estimating scaling parameters using several benchmark datasets for classification. Our proposed feature scaling method consistently outperforms more complex methods on all of the benchmark datasets and improves classification accuracy of a state-of-the-art online classification algorithm. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Knowledge-Based Systems is the property of Elsevier B.V. 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=123371874 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.knosys.2017.05.010 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 97 Subjects: – SubjectFull: Distance education Type: general – SubjectFull: Telecourses Type: general – SubjectFull: Online education Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Comprehension Type: general Titles: – TitleFull: Dynamic feature scaling for online learning of binary classifiers. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bollegala, Danushka IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 09507051 Numbering: – Type: volume Value: 129 Titles: – TitleFull: Knowledge-Based Systems Type: main |
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