Dobór zmiennych do zagregowanych modeli dyskryminacyjnych z wykorzystaniem algorytmów genetycznych - Dudek, Andrzej; Kurzydłowski, Adam (Selection of variables into aggregated discriminant models with usage of genetic algorithms, 2007)
In discriminant analysis studies models using single classification trees are often replaced by models aggregating partial models into one multiple model. Breiman [ 1996] showed that significant reduction of classification error can be achieved when independency of partial models is fulfilld. So key role in such approach plays appropriate selection of objects or variables of subsets of training set. Among methods of selection of objects into aggregated models the most often used are: boosting, bagging, adaptive bagging, arcing, windowing. Among methods of selection of variables into aggregated models Correlation-based Feature Selection (CFS) developed by Hall [2000] was most effective. Gatnar [2003] proposed Correlation-based Feature Selection based on Hellwig Heuristic (CFSH) method and empirically showed that CFSH gave smaller classification errors that CFS. In this paper some modification of CFSH is proposed and genetic algorithms are used to find best subsets of variables in partial models. Classification errors for CFFS, CFSH and modified method are compared on datasets from University o f California Repository o f Machine Learning.
Prace Naukowe Akademii Ekonomicznej we Wrocławiu; 2007; nr 1189, s. 147-155