Genetic Programming of Heterogeneous Ensembles for Classification
The ensemble classification paradigm is an effective way to improve the performance and stability of individual predictors. Many ways to build ensembles have been proposed so far, most notably bagging and boosting based techniques. Evolutionary algorithms (EAs) also have been widely used to generate ensembles. In the context of heterogeneous ensembles EAs have been successfully used to adjust weights of base classifiers or to select ensemble members. Usually, a weighted sum is used for combining classifiers outputs in both classical and evolutionary approaches. This study proposes a novel genetic program that learns a fusion function for combining heterogeneous-classifiers outputs. It evolves a population of fusion functions in order to maximize the classification accuracy. Highly non-linear functions are obtained with the proposed method, subsuming the existing weighted-sum formulations. Experimental results show the effectiveness of the proposed approach, which can be used not only with heterogeneous classifiers but also with homogeneous-classifiers and under bagging/boosting based formulations.
KeywordsHeterogeneous ensembles Genetic programming
- 2.Kuncheva, L., Whitaker, C.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach. Learn. 51(2), 181–207 (2003)Google Scholar
- 6.de Oliveira, D., Canuto, A., De Souto, M.C.P.: Use of multi-objective genetic algorithms to investigate the diversity/accuracy dilemma in heterogeneous ensembles. In: Proc. of IJCNN, pp. 2339–2346 (2010)Google Scholar
- 12.Escalante, H.J., Montes, M., Sucar, L.E.: Ensemble particle swarm model selection. In: Proc. of IJCNN, pp. 1–10 (2010)Google Scholar
- 14.Langdon, W.B., Poli, R.: Foundations of Genetic Programming. Springer (2001)Google Scholar