Conference proceeding
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, pp.377-382
KDD '01
08/26/2001
DOI: 10.1145/502512.502568
Abstract
Ensemble methods have recently garnered a great deal of attention in the machine learning community. Techniques such as Boosting and Bagging have proven to be highly effective but require repeated resampling of the training data, making them inappropriate in a data mining context. The methods presented in this paper take advantage of plentiful data, building separate classifiers on sequential chunks of training points. These classifiers are combined into a fixed-size ensemble using a heuristic replacement strategy. The result is a fast algorithm for large-scale or streaming data that classifies as well as a single decision tree built on all the data, requires approximately constant memory, and adjusts quickly to concept drift.
Details
- Title: Subtitle
- A streaming ensemble algorithm (SEA) for large-scale classification
- Creators
- W StreetYongSeog Kim
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, pp.377-382
- Publisher
- ACM
- Series
- KDD '01
- DOI
- 10.1145/502512.502568
- Language
- English
- Date published
- 08/26/2001
- Academic Unit
- Bus Admin College; Nursing; Computer Science; Business Analytics
- Record Identifier
- 9984380435302771
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