Relational information gain.
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| Title: | Relational information gain. |
|---|---|
| Authors: | Lippi, Marco1 lippi@dsi.unifi.it, Jaeger, Manfred2, Frasconi, Paolo1, Passerini, Andrea3 |
| Source: | Machine Learning. May2011, Vol. 83 Issue 2, p219-239. 21p. |
| Subjects: | Electronic data processing, Horn clauses, Entropy (Information theory), Search algorithms, Decision trees, Data modeling |
| Abstract: | We introduce relational information gain, a refinement scoring function measuring the informativeness of newly introduced variables. The gain can be interpreted as a conditional entropy in a well-defined sense and can be efficiently approximately computed. In conjunction with simple greedy general-to-specific search algorithms such as FOIL, it yields an efficient and competitive algorithm in terms of predictive accuracy and compactness of the learned theory. In conjunction with the decision tree learner TILDE, it offers a beneficial alternative to lookahead, achieving similar performance while significantly reducing the number of evaluated literals. [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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 59809641 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Relational information gain. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lippi%2C+Marco%22">Lippi, Marco</searchLink><relatesTo>1</relatesTo><i> lippi@dsi.unifi.it</i><br /><searchLink fieldCode="AR" term="%22Jaeger%2C+Manfred%22">Jaeger, Manfred</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Frasconi%2C+Paolo%22">Frasconi, Paolo</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Passerini%2C+Andrea%22">Passerini, Andrea</searchLink><relatesTo>3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Machine+Learning%22">Machine Learning</searchLink>. May2011, Vol. 83 Issue 2, p219-239. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Horn+clauses%22">Horn clauses</searchLink><br /><searchLink fieldCode="DE" term="%22Entropy+%28Information+theory%29%22">Entropy (Information theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Search+algorithms%22">Search algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+trees%22">Decision trees</searchLink><br /><searchLink fieldCode="DE" term="%22Data+modeling%22">Data modeling</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We introduce relational information gain, a refinement scoring function measuring the informativeness of newly introduced variables. The gain can be interpreted as a conditional entropy in a well-defined sense and can be efficiently approximately computed. In conjunction with simple greedy general-to-specific search algorithms such as FOIL, it yields an efficient and competitive algorithm in terms of predictive accuracy and compactness of the learned theory. In conjunction with the decision tree learner TILDE, it offers a beneficial alternative to lookahead, achieving similar performance while significantly reducing the number of evaluated literals. [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/s10994-010-5194-7 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 219 Subjects: – SubjectFull: Electronic data processing Type: general – SubjectFull: Horn clauses Type: general – SubjectFull: Entropy (Information theory) Type: general – SubjectFull: Search algorithms Type: general – SubjectFull: Decision trees Type: general – SubjectFull: Data modeling Type: general Titles: – TitleFull: Relational information gain. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lippi, Marco – PersonEntity: Name: NameFull: Jaeger, Manfred – PersonEntity: Name: NameFull: Frasconi, Paolo – PersonEntity: Name: NameFull: Passerini, Andrea IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2011 Type: published Y: 2011 Identifiers: – Type: issn-print Value: 08856125 Numbering: – Type: volume Value: 83 – Type: issue Value: 2 Titles: – TitleFull: Machine Learning Type: main |
| ResultId | 1 |
