Subgroup Discovery for Weight Learning in Breast Cancer Diagnosis

Autor:	Joaquim Melendez, Beatriz López, Joan Brunet, Víctor Barrera, Carles Pous, J. Sanz
Rok vydání:	2009
Předmět:	Computer science business.industry Specific knowledge Machine learning computer.software_genre medicine.disease Attribute weight Breast cancer Similarity (psychology) medicine Relevance (information retrieval) Data mining Artificial intelligence business computer
Zdroj:	Artificial Intelligence in Medicine ISBN: 9783642029752 AIME
DOI:	10.1007/978-3-642-02976-9_49
Popis:	In the recent years, there is an increasing interest of the use of case-based reasoning (CBR) in medicine. CBR is an approach to problem solving that is able to use specific knowledge of previous experiences. However, the efficiency of CBR strongly depends on the similarity metrics used to recover past experiences. In such metrics, the role of attribute weights is critical. In this paper we propose a methodology that use subgroup discovery methods to learn the relevance of the attributes. The methodology is applied to a Breast Cancer dataset obtaining significant improvements. ...
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::805419365b3a57f813ddaa26ffeda781 https://doi.org/10.1007/978-3-642-02976-9_49 Zobrazit plný text záznamu