Ensembles of Similarity-Based Models
Włodzisław Duch, Karol Grudziński · Advances in intelligent and soft computing · 2001
Ensembles of independent classifiers are usually more accurate and show smaller variance than individual classifiers. Methods of selection of Similarity Based Models (SBM) that should be included in an ensemble are discussed. Standard k -NN, weighted k -NN, ensembles of weighted models and ensembles of averaged weighted models are considered. Ensembles of competent models are introduced. Results of numerical experiments on benchmark and real-world datasets are presented. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.