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Extremely randomized quantile forests

Matjaž Balon (2016) Extremely randomized quantile forests. EngD thesis.

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    Extremely randomized quantile forests are an ensemble method which extends ordinary random forests with additional randomness and quantiles. In this work we checked its prediction accuracy with different success measures and execution speed. We also analyzed influences of various parameters and size of learning dataset on prediction performance and time needed for execution. We compared method with different success measures and execution time also with two methods that use quantiles for prediction and three methods that give prediction intervals. Extremely randomized quantile forests have been proved as competitive in terms of prediction strength with different measures and also in speed of execution.

    Item Type: Thesis (EngD thesis)
    Keywords: machine learning, quantiles, random forest, regression
    Number of Pages: 41
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    prof. dr. Igor Kononenko237Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1536942275)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 3335
    Date Deposited: 17 May 2016 16:08
    Last Modified: 24 May 2016 08:40
    URI: http://eprints.fri.uni-lj.si/id/eprint/3335

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