Multistrategy ensemble learning: reducing error by combining ensemble learning techniques

Ensemble learning strategies, especially boosting and bagging decision trees, have demonstrated impressive capacities to improve the prediction accuracy of base learning algorithms. Further gains have been demonstrated by strategies that combine simple ensemble formation approaches. We investigate t...

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Bibliographic Details
Published inIEEE transactions on knowledge and data engineering Vol. 16; no. 8; pp. 980 - 991
Main Authors Webb, G.I., Zheng, Z.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.08.2004
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Accuracy
Algorithms
Bagging
bias
Boosting
committee learning
Computer errors
Decision trees
Diversity reception
ensemble diversity
ensemble learning
Error analysis
Error correction
Errors
Gain
Learning
multiboosting
Strategy
Testing
variance
Voting
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Summary:Ensemble learning strategies, especially boosting and bagging decision trees, have demonstrated impressive capacities to improve the prediction accuracy of base learning algorithms. Further gains have been demonstrated by strategies that combine simple ensemble formation approaches. We investigate the hypothesis that the improvement in accuracy of multistrategy approaches to ensemble learning is due to an increase in the diversity of ensemble members that are formed. In addition, guided by this hypothesis, we develop three new multistrategy ensemble learning techniques. Experimental results in a wide variety of natural domains suggest that these multistrategy ensemble learning techniques are, on average, more accurate than their component ensemble learning techniques.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2004.29