@inproceedings{0ac9d17da2a94d54be103493cfe99f06,
title = "Analysis of diversity methods for evolutionary multi-objective ensemble classifiers",
abstract = "Ensemble classifiers are strong and robust methods for classification and regression tasks. Considering the balance between runtime and classifier accuracy the learning problem becomes a multi-objective optimization problem. In this work, we propose an evolutionary multiobjective algorithm based on non-dominated sorting that balances runtime and accuracy properties of nearest neighbor classifier ensembles and decision tree ensembles. We identify relevant ensemble parameters with a significant impact on the accuracy and runtime. In the experimental part of this paper, we analyze the behavior on typical classification benchmark problems.",
keywords = "Ensemble classification, Multi-objective optimization",
author = "Stefan Oehmcke and Justin Heinermann and Oliver Kramer",
year = "2015",
month = jan,
day = "1",
doi = "10.1007/978-3-319-16549-3_46",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag,",
pages = "567--578",
editor = "Giovanni Squillero and Mora, {Antonio M.}",
booktitle = "Applications of Evolutionary Computation - 18th European Conference, EvoApplications 2015, Proceedings",
note = "18th European Conference on the Applications of Evolutionary Computation, EvoApplications 2015 ; Conference date: 08-04-2015 Through 10-04-2015",
}