| DOI | Trouver le DOI : https://doi.org/10.1504/IJCBDD.2008.021422 |
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| Auteur | Rechercher : Djebbari, Amira1; Rechercher : Liu, Ziying1; Rechercher : Phan, Sieu1; Rechercher : Famili, Fazel1 |
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| Affiliation | - Conseil national de recherches du Canada. Institut de technologie de l'information du CNRC
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| Format | Texte, Article |
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| Sujet | computational biology; machine learning; data mining; knowledge discovery; bioinformatics; breast cancer prognosis; survival prediction; classification performance; sensitivity |
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| Résumé | Current breast cancer predictive signatures are not unique. Can we use this fact to our advantage to improve prediction? From the machine learning perspective, it is well known that combining multiple classifiers can improve classification performance. We propose an ensemble machine learning approach which consists of choosing feature subsets and learning predictive models from them. We then combine models based on certain model fusion criteria and we also introduce a tuning parameter to control sensitivity. Our method significantly improves classification performance with a particular emphasis on sensitivity which is critical to avoid misclassifying poor prognosis patients as good prognosis. |
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| Date de publication | 2008 |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Numéro NPARC | 23000650 |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 8144431a-396f-4818-b3f7-9eaf614d1011 |
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| Enregistrement créé | 2016-08-17 |
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| Enregistrement modifié | 2020-04-15 |
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