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type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #76421 Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles
sameAs	Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles
http://eprints.org/ontology/hasAccepted	Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles (PDF)
http://eprints.org/ontology/hasDocument	Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles (PDF) Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles (Other) Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles (Other) Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles (Other) Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles (Other) Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles (Other)
dc:hasVersion	Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles (PDF)
Title	Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles
described by	https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2019-05-15
Creator	Utkarsh Agrawal Jonathan Garibaldi Emad A. Rakha John M.S. Bartlett Christian Wagner David Cameron Ian O. Ellis D. Soria Andrew R. Green
status	peer reviewed published
Publisher	Elsevier
abstract	Breast Cancer is one of the most common causes of cancer death in women, representing a very complex disease with varied molecular alterations. To assist breast cancer prognosis, the classification of patients into biological groups is of great significance for treatment strategies. Recent studies have used an ensemble of multiple clustering algorithms to elucidate the most characteristic biological groups of breast cancer. However, the combination of various clustering methods resulted in a number of patients remaining unclustered. Therefore, a framework still needs to be developed which can assign as many unclustered (i.e. biologically diverse) patients to one of the identified groups in order to improve classification. Therefore, in this paper we develop a novel classification framework which introduces a new ensemble classification stage after the ensemble clustering stage to target the unclustered patients. Thus, a step-by-step pipeline is introduced which couples ensemble clustering with ensemble classification for the identification of core groups, data distribution in them and improvement in final classification results by targeting the unclustered data. The proposed pipeline is employed on a novel real world breast cancer dataset and subsequently its robustness and stability are examined by testing it on standard datasets. The results show that by using the presented framework, an improved classification is obtained. Finally, the results have been verified using statistical tests, visualisation techniques, cluster quality assessment and interpretation from clinical experts.
Is Part Of	Artificial Intelligence in Medicine https://kar.kent.ac.uk/id/repository
Subject	R858 Computer applications to medicine. Medical informatics. Medical information technology
list of authors	https://kar.kent.ac.uk/id/eprint/76421#authors
volume	97
is topic of	https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #76421 Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles

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