Publication
Improving recall values in breast cancer diagnosis with Incremental Background Knowledge
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | |
| datacite.subject.sdg | 03:Saúde de Qualidade | |
| dc.contributor.author | Silva, Catarina | |
| dc.contributor.author | Ribeiro, Bernardete | |
| dc.contributor.author | Lopes, Noel | |
| dc.date.accessioned | 2025-12-16T14:46:50Z | |
| dc.date.available | 2025-12-16T14:46:50Z | |
| dc.date.issued | 2010-07 | |
| dc.description | EISBN - 978-1-4244-6918-5 | |
| dc.description | Conference date - 18 July 2010 - 23 July 2010; Conference code - 85188 | |
| dc.description.abstract | Cancer diagnosis is generally the process of using some form of physical or genetic tests or exams, usually referred as patient data, to detect the disease. One of the main problems with cancer diagnosis systems is the lack of labeled data, as well as the difficulties of labeling pre-existing unlabeled data. Thus, there is a growing interest in exploring the use of unlabeled data as a way to improve classification performance in cancer diagnosis. The possible availability of this kind of data for some applications makes it an appealing source of information. In this work we explore an Incremental Background Knowledge (IBK) technique to introduce unlabeled data into the training set by expanding it using initial classifiers to better aid decisions, namely by improving recall values. The defined incremental SVM margin-based method was tested in the Wisconsin-Madison breast cancer diagnosis problem to examine the effectiveness of such techniques in supporting diagnosis. | eng |
| dc.identifier.citation | C. Silva, B. Ribeiro and N. Lopes, "Improving recall values in breast cancer diagnosis with Incremental Background Knowledge," The 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain, 2010, pp. 1-6, doi: https://doi.org/10.1109/IJCNN.2010.5596641. | |
| dc.identifier.doi | 10.1109/ijcnn.2010.5596641 | |
| dc.identifier.eissn | 2161-4407 | |
| dc.identifier.isbn | 978-1-4244-6916-1 | |
| dc.identifier.isbn | 978-1-4244-6918-5 | |
| dc.identifier.issn | 2161-4393 | |
| dc.identifier.uri | http://hdl.handle.net/10400.8/15083 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | IEEE Canada | |
| dc.relation.hasversion | https://ieeexplore.ieee.org/document/5596641 | |
| dc.relation.ispartof | The 2010 International Joint Conference on Neural Networks (IJCNN) | |
| dc.rights.uri | N/A | |
| dc.subject | Support vector machines | |
| dc.subject | Training | |
| dc.subject | Breast cancer | |
| dc.subject | Machine learning | |
| dc.subject | Learning systems | |
| dc.subject | Vectors | |
| dc.title | Improving recall values in breast cancer diagnosis with Incremental Background Knowledge | eng |
| dc.type | conference paper | |
| dspace.entity.type | Publication | |
| oaire.citation.conferenceDate | 2010-07 | |
| oaire.citation.conferencePlace | Barcelona, Spain | |
| oaire.citation.title | Proceedings of the International Joint Conference on Neural Networks | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Silva | |
| person.givenName | Catarina | |
| person.identifier.ciencia-id | 1B19-3DDC-BE75 | |
| person.identifier.orcid | 0000-0002-5656-0061 | |
| relation.isAuthorOfPublication | ee28e079-5ca7-4842-9094-372c40f75c38 | |
| relation.isAuthorOfPublication.latestForDiscovery | ee28e079-5ca7-4842-9094-372c40f75c38 |
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- Cancer diagnosis is generally the process of using some form of physical or genetic tests or exams, usually referred as patient data, to detect the disease. One of the main problems with cancer diagnosis systems is the lack of labeled data, as well as the difficulties of labeling pre-existing unlabeled data. Thus, there is a growing interest in exploring the use of unlabeled data as a way to improve classification performance in cancer diagnosis. The possible availability of this kind of data for some applications makes it an appealing source of information. In this work we explore an Incremental Background Knowledge (IBK) technique to introduce unlabeled data into the training set by expanding it using initial classifiers to better aid decisions, namely by improving recall values. The defined incremental SVM margin-based method was tested in the Wisconsin-Madison breast cancer diagnosis problem to examine the effectiveness of such techniques in supporting diagnosis.
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