Empirical Comparisons of Online Boosting Algorithms with Running Time

DOI

10.2991/ic3me-15.2015.154How to use a DOI?

Keywords

boosting, ensemble learning,online learning, running time

Abstract

Boosting is an effective classifier combination method, which can improve classification performance of an unstable learning algorithm due to its theoretical performance guarantees and strong experimental results. However, the algorithm has been used mainly in batch mode, i.e., it requires the entire training set to be available at once and, in some cases, require random access to the data. Recently, Nikunj C.oza(2001) proved that some preliminary theoretical results and some empirical comparisons of the classification accuracies of online algorithms with their corresponding batch algorithms on many datasets. In this paper, we present online versions of some boosting methods that require only one pass through the training data. Specifically, we present theoretical and experimental evidence that our online algorithms succeed in this mirroring, often obtaining classification performance comparable to their batch counterparts in less time. We compare the online and batch algorithms experimentally in terms of running time.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiaowei Sun
PY  - 2015/08
DA  - 2015/08
TI  - Empirical Comparisons of Online Boosting Algorithms with Running Time
BT  - Proceedings of the 3rd International Conference on Material, Mechanical and Manufacturing Engineering
PB  - Atlantis Press
SP  - 804
EP  - 807
SN  - 2352-5401
UR  - https://doi.org/10.2991/ic3me-15.2015.154
DO  - 10.2991/ic3me-15.2015.154
ID  - Sun2015/08
ER  -