@inproceedings{ded4e956460441b2899195e18c48c662,
title = "Hybrid optimization method applied to adaptive splitting and selection algorithm",
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.",
keywords = "Classification, Classifier ensemble, Cross entropy, Genetic algorithms, Hybrid optimization, Machine learning",
author = "Pedro Lopez-Garcia and Micha{\l} Wo{\'z}niak and Enrique Onieva and Asier Perallos",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 11th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2016 ; Conference date: 08-04-2016 Through 20-04-2016",
year = "2016",
doi = "10.1007/978-3-319-32034-2\_62",
language = "English",
isbn = "9783319320335",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "742--750",
editor = "Francisco Martinez-Alvarez and Alicia Troncoso and Hector Quintian and Emilio Corchado",
booktitle = "Hybrid Artificial Intelligent Systems - 11th International Conference, HAIS 2016, Proceedings",
address = "Germany",
}