A new set of Random Forests with varying dynamic data reduction and voting techniques

Hussein Mohsen, Hasan Kurban, Mark Jenne, Mehmet Dalkilic

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

3 Citations (Scopus)

Abstract

Random forests have been used as effective models to tackle a number of classification and regression problems. In this paper, we present a new type of Random Forests (RFs) called Red(uced)-RF that adopts a new voting mechanism called Priority Vote Weighting (PV) and a new dynamic data reduction principle which improve accuracy and execution time compared to Breiman's conventional RF. Red-RF also shows that the strength of a random forest can increase without noticeably increasing correlation between the trees. We then compare performance of Red-RF, 9 new RF variants and Breiman's RF in eight experiments that involve classification problems with datasets of different sizes.

Original languageEnglish
Title of host publicationDSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics
EditorsGeorge Karypis, Longbing Cao, Wei Wang, Irwin King
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages399-405
Number of pages7
ISBN (Electronic)9781479969913
DOIs
Publication statusPublished - 10 Mar 2014
Externally publishedYes
Event2014 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2014 - Shanghai, China
Duration: 30 Oct 20141 Nov 2014

Publication series

NameDSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics

Conference

Conference2014 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2014
Country/TerritoryChina
CityShanghai
Period30/10/141/11/14

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