@inproceedings{4b9863c90ef54942989845712758057c,
title = "Algorithm selection on data streams",
abstract = "We explore the possibilities of meta-learning on data streams, in particular algorithm selection. In a first experiment we calculate the characteristics of a small sample of a data stream, and try to predict which classifier performs best on the entire stream. This yields promising results and interesting patterns. In a second experiment, we build a meta-classifier that predicts, based on measurable data characteristics in a window of the data stream, the best classifier for the next window. The results show that this meta-algorithm is very competitive with state of the art ensembles, such as OzaBag, OzaBoost and Leveraged Bagging. The results of all experiments are made publicly available in an online experiment database, for the purpose of verifiability, reproducibility and generalizability.",
author = "{Rijn, van}, J.R. and G. Holmes and B. Pfahringer and J. Vanschoren",
year = "2014",
doi = "10.1007/978-3-319-11812-3_28",
language = "English",
isbn = "978-3-319-11811-6",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "325--336",
editor = "S. Dzeroski and P. Panov and D. Kocev and L. Todorovski",
booktitle = "Discovery Science (17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014. Proceedings)",
address = "Germany",
note = "conference; 17th International Conference on Discovery Science; 2014-10-08; 2014-10-10 ; Conference date: 08-10-2014 Through 10-10-2014",
}