Abstract
The paper presents an approach to train combined classifiers based on feature space splitting and selection of the best classifier ensemble to each subspace of feature space. The learning method uses a hybrid algorithm that combines a Genetic Algorithm and Cross Entropy Method. The proposed approach was evaluated on the basis of the comprehensive computer experiments run on balanced and imbalanced datasets, and compared with Cluster and Selection algorithm, improving the results obtained by this technique.
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Acknowledgment
Pedro Lopez-Garcia, Enrique Onieva and Asier Perallos’ work was supported by TIMON Project (Enhanced real time services for an optimized multimodal mobility relying on cooperative networks and open data). This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No. 636220. Also, Michal Wozniak’s work was supported by the Polish National Science Centre under the grant no. DEC-2013/09/B/ST6/02264 and by EC under FP7, Coordination and Support Action, Grant Agreement Number 316097, ENGINE - European Research Centre of Network Intelligence for Innovation Enhancement (http://engine.pwr.wroc.pl/). All computer experiments were carried out using computer equipment sponsored by ENGINE project.
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Lopez-Garcia, P., Woźniak, M., Onieva, E., Perallos, A. (2016). Hybrid Optimization Method Applied to Adaptive Splitting and Selection Algorithm. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_62
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DOI: https://doi.org/10.1007/978-3-319-32034-2_62
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