Classification with costly features in hierarchical deep sets.
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| Title: | Classification with costly features in hierarchical deep sets. |
|---|---|
| Authors: | Janisch, Jaromír1 (AUTHOR) jaromir.janisch@fel.cvut.cz, Pevný, Tomáš1 (AUTHOR), Lisý, Viliam1 (AUTHOR) |
| Source: | Machine Learning. Jul2024, Vol. 113 Issue 7, p4487-4522. 36p. |
| Subjects: | Deep reinforcement learning, XML (Extensible Markup Language), Classification, Reinforcement learning |
| Abstract: | Classification with costly features (CwCF) is a classification problem that includes the cost of features in the optimization criteria. Individually for each sample, its features are sequentially acquired to maximize accuracy while minimizing the acquired features' cost. However, existing approaches can only process data that can be expressed as vectors of fixed length. In real life, the data often possesses rich and complex structure, which can be more precisely described with formats such as XML or JSON. The data is hierarchical and often contains nested lists of objects. In this work, we extend an existing deep reinforcement learning-based algorithm with hierarchical deep sets and hierarchical softmax, so that it can directly process this data. The extended method has greater control over which features it can acquire and, in experiments with seven datasets, we show that this leads to superior performance. To showcase the real usage of the new method, we apply it to a real-life problem of classifying malicious web domains, using an online service. [ABSTRACT FROM AUTHOR] |
| Copyright of Machine Learning 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: 177598373 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Classification with costly features in hierarchical deep sets. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Janisch%2C+Jaromír%22">Janisch, Jaromír</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jaromir.janisch@fel.cvut.cz</i><br /><searchLink fieldCode="AR" term="%22Pevný%2C+Tomáš%22">Pevný, Tomáš</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lisý%2C+Viliam%22">Lisý, Viliam</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Machine+Learning%22">Machine Learning</searchLink>. Jul2024, Vol. 113 Issue 7, p4487-4522. 36p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+reinforcement+learning%22">Deep reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22XML+%28Extensible+Markup+Language%29%22">XML (Extensible Markup Language)</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Classification with costly features (CwCF) is a classification problem that includes the cost of features in the optimization criteria. Individually for each sample, its features are sequentially acquired to maximize accuracy while minimizing the acquired features' cost. However, existing approaches can only process data that can be expressed as vectors of fixed length. In real life, the data often possesses rich and complex structure, which can be more precisely described with formats such as XML or JSON. The data is hierarchical and often contains nested lists of objects. In this work, we extend an existing deep reinforcement learning-based algorithm with hierarchical deep sets and hierarchical softmax, so that it can directly process this data. The extended method has greater control over which features it can acquire and, in experiments with seven datasets, we show that this leads to superior performance. To showcase the real usage of the new method, we apply it to a real-life problem of classifying malicious web domains, using an online service. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Machine Learning 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/s10994-024-06565-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 36 StartPage: 4487 Subjects: – SubjectFull: Deep reinforcement learning Type: general – SubjectFull: XML (Extensible Markup Language) Type: general – SubjectFull: Classification Type: general – SubjectFull: Reinforcement learning Type: general Titles: – TitleFull: Classification with costly features in hierarchical deep sets. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Janisch, Jaromír – PersonEntity: Name: NameFull: Pevný, Tomáš – PersonEntity: Name: NameFull: Lisý, Viliam IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 08856125 Numbering: – Type: volume Value: 113 – Type: issue Value: 7 Titles: – TitleFull: Machine Learning Type: main |
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