Academic Journal
Evolutionary design of decision trees.
| Τίτλος: | Evolutionary design of decision trees. |
|---|---|
| Συγγραφείς: | Podgorelec, Vili, Šprogar, Matej, Pohorec, Sandi |
| Πηγή: | WIREs: Data Mining & Knowledge Discovery; Mar2013, Vol. 3 Issue 2, p63-82, 20p |
| Περίληψη: | Decision tree (DT) is one of the most popular symbolic machine learning approaches to classification with a wide range of applications. Decision trees are especially attractive in data mining. It has an intuitive representation and is, therefore, easy to understand and interpret, also by nontechnical experts. The most important and critical aspect of DTs is the process of their construction. Several induction algorithms exist that use the recursive top-down principle to divide training objects into subgroups based on different statistical measures in order to achieve homogeneous subgroups. Although being robust and fast, generally providing good results, their deterministic and heuristic nature can lead to suboptimal solutions. Therefore, alternative approaches have developed which try to overcome the drawbacks of classical induction. One of the most viable approaches seems to be the use of evolutionary algorithms, which can produce better DTs as they are searching for globally optimal solutions, evaluating potential solutions with regard to different criteria. We review the process of evolutionary design of DTs, providing the description of the most common approaches as well as referring to recognized specializations. The overall process is first explained and later demonstrated in a step-by-step case study using a dataset from the University of California, Irvine (UCI) machine learning repository. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR] |
| Copyright of WIREs: Data Mining & Knowledge Discovery is the property of Wiley-Blackwell 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Evolutionary design of decision trees. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Podgorelec%2C+Vili%22">Podgorelec, Vili</searchLink><br /><searchLink fieldCode="AR" term="%22Šprogar%2C+Matej%22">Šprogar, Matej</searchLink><br /><searchLink fieldCode="AR" term="%22Pohorec%2C+Sandi%22">Pohorec, Sandi</searchLink> – Name: TitleSource Label: Source Group: Src Data: WIREs: Data Mining & Knowledge Discovery; Mar2013, Vol. 3 Issue 2, p63-82, 20p – Name: Abstract Label: Abstract Group: Ab Data: Decision tree (DT) is one of the most popular symbolic machine learning approaches to classification with a wide range of applications. Decision trees are especially attractive in data mining. It has an intuitive representation and is, therefore, easy to understand and interpret, also by nontechnical experts. The most important and critical aspect of DTs is the process of their construction. Several induction algorithms exist that use the recursive top-down principle to divide training objects into subgroups based on different statistical measures in order to achieve homogeneous subgroups. Although being robust and fast, generally providing good results, their deterministic and heuristic nature can lead to suboptimal solutions. Therefore, alternative approaches have developed which try to overcome the drawbacks of classical induction. One of the most viable approaches seems to be the use of evolutionary algorithms, which can produce better DTs as they are searching for globally optimal solutions, evaluating potential solutions with regard to different criteria. We review the process of evolutionary design of DTs, providing the description of the most common approaches as well as referring to recognized specializations. The overall process is first explained and later demonstrated in a step-by-step case study using a dataset from the University of California, Irvine (UCI) machine learning repository. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of WIREs: Data Mining & Knowledge Discovery is the property of Wiley-Blackwell 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: 10.1002/widm.1079 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 63 Titles: – TitleFull: Evolutionary design of decision trees. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Podgorelec, Vili – PersonEntity: Name: NameFull: Šprogar, Matej – PersonEntity: Name: NameFull: Pohorec, Sandi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2013 Type: published Y: 2013 Identifiers: – Type: issn-print Value: 19424787 Numbering: – Type: volume Value: 3 – Type: issue Value: 2 Titles: – TitleFull: WIREs: Data Mining & Knowledge Discovery Type: main |
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