Model Based Sampling - Fitting an Ensemble of Models into a Single Model
2015 (English)In: Proceedings of 2015 International Conference on Computational Science and Computational Intelligence / [ed] Hamid R. Arabnia, Leonidas Deligiannidis, Quoc-Nam Tran, IEEE Computer Society, 2015, 186-191 p.Conference paper (Refereed)Text
Large ensembles of classifiers usually outperform single classifiers. Unfortunately ensembles have two major drawbacks compared to single classifier; interpretability and classifications times. Using the Combined Multiple Models (CMM) framework for compressing an ensemble of classifiers into a single classifier the problems associated with ensembles can be avoided while retaining almost similar classification power as that of the original ensemble. One open question when using CMM concerns how to generate values that constitute the synthetic example. In this paper we present a novel method for generating synthetic examples by utilizing the structure of the ensemble. This novel method is compared with other methods for generating synthetic examples using the CMM framework. From the comparison it is concluded that the novel method outperform the other methods.
Place, publisher, year, edition, pages
IEEE Computer Society, 2015. 186-191 p.
Machine learning algorithms, Supervised learning, Sampling methods, Approximation algorithms
Research subject Computer and Systems Sciences
IdentifiersURN: urn:nbn:se:su:diva-125142DOI: 10.1109/CSCI.2015.27ISBN: 978-1-4673-9795-7OAI: oai:DiVA.org:su-125142DiVA: diva2:891931
2015 International Conference on Computational Science and Computational Intelligence, 7-9 December 2015, Las Vegas, Nevada, USA