A genetic approach for training diverse classifier ensembles
David Gacquer, Véronique Delcroix, Sylvain Piechowiak · 2008
Classification is an active topic of Machine Learning. The most recent achievements in this domain suggest using ensembles of learners instead of a single classifier to improve classification accuracy. Comparisons between Bagging and Boosting show that classifier ensembles perform better when their members exhibit diversity, that is commit different errors. This paper proposes a genetic algorithm to design classifier ensembles, using a fitness function based on both accuracy and diversity. The proposed implementation has been run on several UCI Machine Learning datasets and compared to the performances obtained with bagging algorithm and a single classifier of the same nature.