@misc{Dudek_Andrzej_Porównanie_2007,
 author={Dudek, Andrzej and Kurzydłowski, Adam},
 year={2007},
 rights={Wszystkie prawa zastrzeżone (Copyright)},
 description={Prace Naukowe Akademii Ekonomicznej we Wrocławiu. Taksonomia (14); 2007; nr 1169, s. 202-209},
 language={pol},
 abstract={In discriminant analysis studies, models using single classification trees are often replaced by models aggregating partial models into one multiple model. Between selection methods of objects into aggregated models boosting, bagging, adaptive bagging, arcing, windowing are most commonly used. The most effective method of selection of variables into aggregated models is Correlation-based Feature Selection (CFS) developed by Hall. Gatnar proposed Correlation-based Feature Selection based on Hellwig Heuristic (CFSH) method and empirically showed that CFSH gives smaller classification errors that CFS. In this paper comparison between CFS, CFSH and modification of CFSH method based on optimization through genetic algorithms is presented.},
 type={artykuł},
 title={Porównanie metod doboru zmiennych do zagregowanych modeli dyskryminacyjnych},
}