Attribute Ranking for Intelligent Data Analysis in Medical Applications (CROSBI ID 540584)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Gamberger, Dragan ; Prcela, Marin ; Bošnjak Matko
engleski
Attribute Ranking for Intelligent Data Analysis in Medical Applications
The work critically analyzes machine learning based attribute selection algorithms from the perspective of their applicability for intelligent data analysis. Different approaches are illustrated by the results obtained by their application on a large medical domain of heart failure patients. Random Forest algorithm, based on voting of many relatively non-correlated classifiers, is accepted as the most reliable approach to attribute ranking. Additionally, it is demonstrated that rule-based machine learning algorithms can be used for feature ranking and that application of rule quality measures with different generality may be very useful for human understanding of the domain.
Intelligent data analysis; Machine learning; Attribute ranking
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Podaci o prilogu
323-328.
2008.
objavljeno
Podaci o matičnoj publikaciji
Lužar-Štiffler, Vesna ; Hlju-Dobrić, Vesna ; Bekić, Zoran
Zagreb: Sveučilišni računski centar Sveučilišta u Zagrebu (Srce)
978-953-7138-12-7
1330-1012
Podaci o skupu
30th International Conference on Information Technology Interfaces
predavanje
23.06.2008-26.06.2008
Dubrovnik, Hrvatska