Brunel University Research Archive(BURA) preserves and enables easy and open access to all
types of digital content. It showcases Brunel's research outputs.
Research contained within BURA is open access, although some publications may be subject
to publisher imposed embargoes. All awarded PhD theses are also archived on BURA.
Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/27032Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Yang, X | - |
| dc.contributor.author | Khushi, M | - |
| dc.contributor.author | Shaukat, K | - |
| dc.coverage.spatial | Gold Coast, Australia | - |
| dc.date.accessioned | 2023-08-23T07:30:15Z | - |
| dc.date.available | 2023-08-23T07:30:15Z | - |
| dc.date.issued | 2020-12-16 | - |
| dc.identifier | ORCID iD: Matloob Khushi https://orcid.org/0000-0001-7792-2327 | - |
| dc.identifier.citation | Yang, X., Khushi, M. and Shaukat, K. (2020) 'Biomarker CA125 Feature Engineering and Class Imbalance Learning Improves Ovarian Cancer Prediction', 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020. Gold Coast, Australia, 16-18 December, pp. 1 - 6. doi: 10.1109/CSDE50874.2020.9411607. | en_US |
| dc.identifier.isbn | 978-1-6654-1974-1 (ebk) | - |
| dc.identifier.isbn | 978-1-6654-2991-7 (PoD) | - |
| dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/27032 | - |
| dc.format.extent | 1 - 6 | - |
| dc.format.medium | Print-Electronic | - |
| dc.language | English | - |
| dc.language.iso | en_US | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
| dc.source | 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) | - |
| dc.source | 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) | - |
| dc.subject | machine learning | en_US |
| dc.subject | feature engineering | en_US |
| dc.subject | class imbalance | en_US |
| dc.subject | ovarian cancer | en_US |
| dc.title | Biomarker CA125 Feature Engineering and Class Imbalance Learning Improves Ovarian Cancer Prediction | en_US |
| dc.type | Article | en_US |
| dc.identifier.doi | https://doi.org/10.1109/CSDE50874.2020.9411607 | - |
| dc.relation.isPartOf | 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020 | - |
| pubs.finish-date | 2020-12-18 | - |
| pubs.finish-date | 2020-12-18 | - |
| pubs.publication-status | Published | - |
| pubs.start-date | 2020-12-16 | - |
| pubs.start-date | 2020-12-16 | - |
| Appears in Collections: | Dept of Computer Science Research Papers | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| FullText.pdf | Copyright © 2020 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works by sending a request to pubs-permissions@ieee.org. For more information, see https:// www.ieee.org/publications/rights/rights-policies.html | 177.79 kB | Adobe PDF | View/Open |
Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.