Dynamic integration with random forests

A. Tsymbal, M. Pechenizkiy, P. Cunningham

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

47 Citations (Scopus)

Abstract

Random Forests (RF) are a successful ensemble prediction technique that uses majority voting or averaging as a combination function. However, it is clear that each tree in a random forest may have a different contribution in processing a certain instance. In this paper, we demonstrate that the prediction performance of RF may still be improved in some domains by replacing the combination function with dynamic integration, which is based on local performance estimates. Our experiments also demonstrate that the RF Intrinsic Similarity is better than the commonly used Heterogeneous Euclidean/Overlap Metric in finding a neighbourhood for local estimates in the context of dynamic integration of classification random forests.
Original languageEnglish
Title of host publicationMachine Learning ECML 2006 : 17th European Conference on Machine Learning Berlin, Germany, September 18-22, 2006 : proceedings
EditorsJ. Fürnkranz, T. Scheffer, M. Spiliopoulou
Place of PublicationBerlin
PublisherSpringer
Pages801-808
ISBN (Print)3-540-45375-X
DOIs
Publication statusPublished - 2006

Publication series

NameLecture Notes in Computer Science
Volume4212
ISSN (Print)0302-9743

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