Advertisement
Published in:
2018 | OriginalPaper | Chapter
Energy-Based Clustering for Pruning Heterogeneous Ensembles
Authors : Javier Cela, Alberto Suárez
Published in: Artificial Neural Networks and Machine Learning – ICANN 2018
Publisher: Springer International Publishing
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Abstract
In this work, an energy-based clustering method is used to prune heterogeneous ensembles. Specifically, the classifiers are grouped according to their predictions in a set of validation instances that are independent from the ones used to build the ensemble. In the empirical evaluation carried out, the cluster that minimizes the error in the validations set, besides reducing computational costs for storage and the prediction times, is almost as accurate as the complete ensemble. Furthermore, it outperforms subensembles that summarize the complete ensemble by including representatives from each of the identified clusters.