Academic Journal
FSelector: a Ruby gem for feature selection.
| Title: | FSelector: a Ruby gem for feature selection. |
|---|---|
| Authors: | Cheng, Tiejun1, Wang, Yanli1, Bryant, Stephen H.1 |
| Source: | Bioinformatics. Nov2012, Vol. 28 Issue 21, p2851-2852. 2p. |
| Subject Terms: | *Ruby (Computer program language), *Feature selection, *Missing data (Statistics), *Bioinformatics, *Machine learning, *Computational biology, *Open source software |
| Abstract: | Summary: The FSelector package contains a comprehensive list of feature selection algorithms for supporting bioinformatics and machine learning research. FSelector primarily collects and implements the filter type of feature selection techniques, which are computationally efficient for mining large datasets. In particular, FSelector allows ensemble feature selection that takes advantage of multiple feature selection algorithms to yield more robust results. FSelector also provides many useful auxiliary tools, including normalization, discretization and missing data imputation.Availability: FSelector, written in the Ruby programming language, is free and open-source software that runs on all Ruby supporting platforms, including Windows, Linux and Mac OS X. FSelector is available from https://rubygems.org/gems/fselector and can be installed like a breeze via the command gem install fselector. The source code is available (https://github.com/need47/fselector) and is fully documented (http://rubydoc.info/gems/fselector/frames).Contact: ywang@ncbi.nlm.nih.gov or bryant@ncbi.nlm.nih.govSupplementary Information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR] |
| Database: | Academic Search Index |
| FullText | Links: – Type: other Text: Availability: 0 |
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| Header | DbId: asx DbLabel: Academic Search Index An: 82772076 RelevancyScore: 1122 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1121.85144042969 |
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| Items | – Name: Title Label: Title Group: Ti Data: FSelector: a Ruby gem for feature selection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cheng%2C+Tiejun%22">Cheng, Tiejun</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Wang%2C+Yanli%22">Wang, Yanli</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Bryant%2C+Stephen+H%2E%22">Bryant, Stephen H.</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Bioinformatics%22">Bioinformatics</searchLink>. Nov2012, Vol. 28 Issue 21, p2851-2852. 2p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Ruby+%28Computer+program+language%29%22">Ruby (Computer program language)</searchLink><br />*<searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br />*<searchLink fieldCode="DE" term="%22Missing+data+%28Statistics%29%22">Missing data (Statistics)</searchLink><br />*<searchLink fieldCode="DE" term="%22Bioinformatics%22">Bioinformatics</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Computational+biology%22">Computational biology</searchLink><br />*<searchLink fieldCode="DE" term="%22Open+source+software%22">Open source software</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Summary: The FSelector package contains a comprehensive list of feature selection algorithms for supporting bioinformatics and machine learning research. FSelector primarily collects and implements the filter type of feature selection techniques, which are computationally efficient for mining large datasets. In particular, FSelector allows ensemble feature selection that takes advantage of multiple feature selection algorithms to yield more robust results. FSelector also provides many useful auxiliary tools, including normalization, discretization and missing data imputation.Availability: FSelector, written in the Ruby programming language, is free and open-source software that runs on all Ruby supporting platforms, including Windows, Linux and Mac OS X. FSelector is available from https://rubygems.org/gems/fselector and can be installed like a breeze via the command gem install fselector. The source code is available (https://github.com/need47/fselector) and is fully documented (http://rubydoc.info/gems/fselector/frames).Contact: ywang@ncbi.nlm.nih.gov or bryant@ncbi.nlm.nih.govSupplementary Information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asx&AN=82772076 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1093/bioinformatics/bts528 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 2 StartPage: 2851 Subjects: – SubjectFull: Ruby (Computer program language) Type: general – SubjectFull: Feature selection Type: general – SubjectFull: Missing data (Statistics) Type: general – SubjectFull: Bioinformatics Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Computational biology Type: general – SubjectFull: Open source software Type: general Titles: – TitleFull: FSelector: a Ruby gem for feature selection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cheng, Tiejun – PersonEntity: Name: NameFull: Wang, Yanli – PersonEntity: Name: NameFull: Bryant, Stephen H. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2012 Type: published Y: 2012 Identifiers: – Type: issn-print Value: 13674803 Numbering: – Type: volume Value: 28 – Type: issue Value: 21 Titles: – TitleFull: Bioinformatics Type: main |
| ResultId | 1 |