Ensembles of Extremely Randomized Trees for Multi-target Regression

@inproceedings{Kocev2015EnsemblesOE,
  title={Ensembles of Extremely Randomized Trees for Multi-target Regression},
  author={Dragi Kocev and Michelangelo Ceci},
  booktitle={Discovery Science},
  year={2015}
}
  • Dragi Kocev, Michelangelo Ceci
  • Published in Discovery Science 2015
  • Computer Science
  • In this work, we address the task of learning ensembles of predictive models for predicting multiple continuous variables, i.e., multi-target regression (MTR. [...] Key Result The results reveal that a multi-target Extra-PCTs ensemble performs statistically significantly better than a single multi-target or single-target PCT. Next, the performance among the different ensemble learning methods is not statistically significantly different, while multi-target Extra-PCTs ensembles are the best performing method…Expand Abstract

    Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv

    Citations

    Publications citing this paper.
    SHOWING 1-4 OF 4 CITATIONS

    Ensembles for multi-target regression with random output selections

    VIEW 7 EXCERPTS
    CITES BACKGROUND, RESULTS & METHODS

    Network representation with clustering tree features

    VIEW 11 EXCERPTS
    CITES METHODS
    HIGHLY INFLUENCED

    Feature Induction and Network Mining with Clustering Tree Ensembles

    VIEW 13 EXCERPTS
    CITES METHODS
    HIGHLY INFLUENCED

    Multi-Label Learning with Deep Forest

    VIEW 1 EXCERPT
    CITES METHODS

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 28 REFERENCES

    Extremely randomized trees

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    Statistical Comparisons of Classifiers over Multiple Data Sets

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    Ensembles for Predicting Structured Outputs

    VIEW 1 EXCERPT