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An empirical comparison of ensemble methods based on classification trees

https://doi.org/10.1080/00949650410001729472
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/3-540-36434-xopenalex-snapshot
Advanced Lectures on Machine Learning
inferredcites →https://doi.org/10.1007/3-540-36434-x_4openalex-snapshot
An Introduction to Boosting and Leveraging
inferredcites →https://doi.org/10.1007/3-540-44581-1_31openalex-snapshot
Smooth Boosting and Learning with Malicious Noise
inferredcites →https://doi.org/10.1007/3-540-45014-9_1openalex-snapshot
Ensemble Methods in Machine Learning
inferredcites →https://doi.org/10.1007/bf00117831openalex-snapshot
Technical Note: Some Properties of Splitting Criteria
inferredcites →https://doi.org/10.1023/a:1007515423169openalex-snapshot
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
inferredcites →https://doi.org/10.1023/a:1007607513941openalex-snapshot
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
inferredcites →https://doi.org/10.1023/a:1007608224229openalex-snapshot
A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms
inferredcites →https://doi.org/10.1023/a:1007618119488openalex-snapshot
Soft Margins for AdaBoost
inferredcites →https://doi.org/10.1023/a:1010852229904openalex-snapshot
An Adaptive Version of the Boost by Majority Algorithm
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
Random Forests
inferredcites →https://doi.org/10.1023/a:1018054314350openalex-snapshot
Bagging Predictors
inferredcites →https://doi.org/10.1214/aos/1024691079openalex-snapshot
Arcing classifier (with discussion and a rejoinder by the author)
inferredcites →https://doi.org/10.1613/jair.614openalex-snapshot
Popular Ensemble Methods: An Empirical Study
inferredcites →https://doi.org/10.2307/2288003openalex-snapshot
Classification and Regression Trees.
inferredcites →https://doi.org/10.2307/2530946openalex-snapshot
Classification and Regression Trees.

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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