Dobór zmiennych do zagregowanych modeli dyskryminacyjnych - Gatnar, Eugeniusz (Feature Selection to Aggregated Classification Models, 2005)
Significant improvement of classification accuracy can be obtained by aggregation of multiple models. Known methods in this field are mostly based on sampling cases from the training set, or changing weights for cases. Further reduction of classification error can be achieved by random selection of variables to the training subsamples or directly to the model. In this paper we propose a new correlation-based feature selection method for classifier ensembles (CFSH) that is contextual (uses feature intercorrelations) and based on the Hellwig heuristic. It gives more accurate aggregated models than those built with other correlation-based feature selection methods.
Prace Naukowe Akademii Ekonomicznej we Wrocławiu. Taksonomia (12); 2005; nr 1076, s. 79-86