In this paper the application of ensembles of instance selection algorithms to improve the quality of dataset size reduction is evaluated. In order to ensure diversity of sub models, selection of a feature subsets was considered. In the experiments the Condensed Nearest Neighbor (CNN) and Edited Nearest Neighbor (ENN) algorithms were evaluated as basic instance selection methods. The results show that it is possible to obtain various trade-offs between data compression and classification accuracy depending on the acceptance threshold and feature ratio parameters. In some cases it was possible to achieve both: higher compression and higher accuracy than those of an individual instance selection algorithm.
In REF , Blachnik evaluated the performance of an ensemble of instance selection algorithms, which they then combined with the aim of improving the quality of dataset size reduction.
Ensembles of instance selection methods based on feature subset
KES
Computer Science