Mixing Hetero- and Homogeneous Models in Weighted Ensembles
The effectiveness of ensembling for improving classification performance is well documented. Broadly speaking, ensemble design can be expressed as a spectrum where at one end a set of heterogeneous classifiers model the same data, and at the other homogeneous models derived from the same classification algorithm are diversified through data manipulation. The cross-validation accuracy weighted probabilistic ensemble is a heterogeneous weighted ensemble scheme that needs reliable estimates of error from its base classifiers. It estimates error through a cross-validation process, and raises the estimates to a power to accentuate differences. We study the effects of maintaining all models trained during cross-validation on the final ensemble’s predictive performance, and the base model’s and resulting ensembles’ variance and robustness across datasets and resamples. We find that augmenting the ensemble through the retention of all models trained provides a consistent and significant improvement, despite reductions in the reliability of the base models’ performance estimates.
KeywordsClassification Ensembles Heterogeneous Homogeneous
This work is supported by the UK Engineering and Physical Sciences Research Council (EPSRC) [grant number EP/M015807/1] and Biotechnology and Biological Sciences Research Council (BBSRC) Norwich Research Park Biosciences Doctoral Training Partnership [grant number BB/M011216/1]. The experiments were carried out on the High Performance Computing Cluster supported by the Research and Specialist Computing Support service at the University of East Anglia.
- 4.Caruana, R., Niculescu-Mizil, A.: Ensemble selection from libraries of models. In: Proceedings of the 21st International Conference on Machine Learning (2004)Google Scholar
- 7.Gashler, M., Giraud-Carrier, C., Martinez, T.: Decision tree ensemble: small heterogeneous is better than large homogeneous. In: 2008 Seventh International Conference on Machine Learning and Applications, pp. 900–905 (2008). https://doi.org/10.1109/ICMLA.2008.154. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4796917
- 8.Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp. 1137–1143. Morgan Kaufmann Publishers Inc. (1995)Google Scholar
- 11.Partalas, I., Tsoumakas, G., Vlahavas, I.: A study on greedy algorithms for ensemble pruning. Aristotle University of Thessaloniki, Thessaloniki, Greece (2012)Google Scholar