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Fast and Light Boosting for Adaptive Mining of Data Streams (2004)
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| Venue: | In PAKDD |
| Citations: | 34 - 6 self |
BibTeX
@INPROCEEDINGS{Chu04fastand,
author = {Fang Chu and Carlo Zaniolo},
title = {Fast and Light Boosting for Adaptive Mining of Data Streams},
booktitle = {In PAKDD},
year = {2004},
pages = {282--292},
publisher = {Springer Verlag}
}
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Abstract
Supporting continuous mining queries on data streams requires algorithms that (i) are fast, (ii) make light demands on memory resources, and (iii) are easily to adapt to concept drift. We propose a novel boosting ensemble method that achieves these objectives. The technique is based on a dynamic sample-weight assignment scheme that achieves the accuracy of traditional boosting without requiring multiple passes through the data. The technique assures faster learning and competitive accuracy using simpler base models. The scheme is then extended to handle concept drift via change detection. The change detection approach aims at significant data changes that could cause serious deterioration of the ensemble performance, and replaces the obsolete ensemble with one built from scratch. Experimental results confirm the advantages of our adaptive boosting scheme over previous approaches.
Keyphrases
data stream adaptive mining light boosting competitive accuracy traditional boosting multiple pass novel boosting ensemble method ensemble performance change detection approach continuous mining query obsolete ensemble memory resource light demand concept drift serious deterioration adaptive boosting scheme simpler base model change detection previous approach experimental result dynamic sample-weight assignment scheme significant data change
