A streaming ensemble algorithm (SEA) for large-scale classification

A streaming ensemble algorithm (SEA) for large-scale classification (2001)

Cached

Download Links

[magna.cs.ucla.edu]
[dollar.biz.uiowa.edu]

Other Repositories/Bibliography

DBLP

by W. Nick Street

Citations:

79 - 1 self

BibTeX

@INPROCEEDINGS{Street01astreaming,
    author = {W. Nick Street},
    title = {A streaming ensemble algorithm (SEA) for large-scale classification},
    booktitle = {},
    year = {2001},
    pages = {377--382},
    publisher = {ACM Press}
}

Years of Citing Articles

Bookmark

OpenURL

Abstract

Classification

Citations

4071	C4.5: Programs for machine learning - Quinlan - 1993
2529	C.J.E.: Uci repository of machine learning databases, http://www.ics.uci.edu/ ∼ mlearn/MLRepository.html - Blake, Keogh
1998	Bagging predictors - Breiman - 1996
1325	Experiments with a new boosting algorithm - Freund, Schapire - 1996
774	Empirical Analysis of Predictive Algorithms for Collaborative Filtering - Breese, Heckerman, et al. - 1998
554	The strenght of weak learnability - Schapire - 1990
449	Kohavi R: An empirical comparison of voting classification algorithms - Bauer - 1999
232	Arcing classifiers - Breiman - 1998
220	Mining high-speed data streams - Domingos, Hulten - 2000
151	Popular ensemble methods: An empirical study - Opitz, Maclin - 1999
140	Scaling up the accuracy of Naive-Bayes classifiers: A decisiontree hybrid - Kohavi - 1996
135	M.: Learning in the presence of concept drift and hidden contexts - Widmer, Kubat - 1996
121	Methods for combining experts’ probability assessments - Jacobs - 1995
97	W.: Boat–optimistic decision tree construction - Gehrke, Ganti, et al. - 1999
51	Feature selection for ensembles - Opitz - 1999
47	On the accuracy of meta-learning for scalable data mining - Chan, Stolfo - 1997
47	Learning with ensembles: How overfitting can be useful - Sollich, Krogh - 1996
37	An empirical comparison of voting classi algorithms: Bagging, boosting and variants - Bauer, Kohavi - 1999
25	Arcing classi - Breiman - 1998
23	Data selection for support vector machine classifiers. Data Mining Institute Technical Report 00-02, February 2000 - Fung, Mangasarian - 2000
19	Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases - CARTER, ALLEN, et al. - 1989
10	Distributed learning on very large data sets - Hall, Bowyer, et al. - 2000
8	Massive support vector regression - Mangasarian, Musicant - 1999
7	Scaling Up the Accuracy of Naive{Bayes Classi a Decision{Tree Hybrid - Kohavi - 1996
2	Learning with ensembles: How over can be useful - Sollich, Krogh - 1996