Extremely randomized quantile forests

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

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Abstract

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:

Language of Content:

Slovenian

Mentor / Comentors:

Name and Surname	ID	Function
prof. dr. Igor Kononenko	237	Mentor

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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