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DC Field | Value | Language |
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dc.contributor.author | Zawbaa, HM | - |
dc.contributor.author | Emary, E | - |
dc.contributor.author | Grosan, C | - |
dc.date.accessioned | 2016-09-23T15:34:11Z | - |
dc.date.available | 2016-03-10 | - |
dc.date.available | 2016-09-23T15:34:11Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | PLOS ONE,11 (3): pp. e0150652 - e0150652, (2016) | en_US |
dc.identifier.issn | 1932-6203 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/13225 | - |
dc.description.abstract | Selecting a subset of relevant properties from a large set of features that describe a dataset is a challenging machine learning task. In biology, for instance, the advances in the available technologies enable the generation of a very large number of biomarkers that describe the data. Choosing the more informative markers along with performing a high-accuracy classification over the data can be a daunting task, particularly if the data are high dimensional. An often adopted approach is to formulate the feature selection problem as a biobjective optimization problem, with the aim of maximizing the performance of the data analysis model (the quality of the data training fitting) while minimizing the number of features used. | en_US |
dc.description.sponsorship | This work was partially supported by the IPROCOM Marie Curie initial training network, funded through the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013/ under REA grants agreement No. 316555, and by the Romanian National Authority for Scientific Research, CNDIUEFISCDI, project number PN-II-PT-PCCA-2011-3.2- 0917. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. | en_US |
dc.format.extent | e0150652 - e0150652 | - |
dc.language.iso | en | en_US |
dc.publisher | PLOS | en_US |
dc.title | Feature Selection via Chaotic Antlion Optimization | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1371/journal.pone.0150652 | - |
dc.relation.isPartOf | PLOS ONE | - |
pubs.issue | 3 | - |
pubs.publication-status | Published | - |
pubs.volume | 11 | - |
Appears in Collections: | Dept of Computer Science Research Papers |
Files in This Item:
File | Description | Size | Format | |
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Fulltext.pdf | 1.82 MB | Adobe PDF | View/Open |
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