@misc{Widz_Sebastian_Techniki_2015,
 author={Widz, Sebastian},
 copyright={Creative Commons Attribution BY 4.0 license},
 address={Warszawa},
 journal={Książka = Book},
 howpublished={online},
 year={2015},
 publisher={Instytut Badań Systemowych. Polska Akademia Nauk},
 publisher={Systems Research Institute. Polish Academy of Sciences},
 language={eng},
 abstract={We consider classifiers ensembles constructed using boosting methodology. Each weak classifier is based on rough set inspired approach to deriving attribute subsets from data organized in a form of a decision system. We focus on approximate decision reducts calculated over universe of weighted objects. We show how methods derived in our previous research integrate smoothly with boosting approach. Two different approaches for approximate reduct calculation are analyzed. We discuss how our framework can be utilized for deriving meaningful attribute subset ensembles. Finally we test our methods on the benchmark data.},
 type={Text},
 title={Techniki informacyjne teoria i zastosowania * Wybrane problemy * Boosting approximate reducts},
 URL={http://www.rcin.org.pl/Content/198695/PDF/KS-2015-03-T5P07.pdf},
}