@inproceedings{0ab74dc25bcb4143935da112f702337d,
title = "A new set of Random Forests with varying dynamic data reduction and voting techniques",
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.",
author = "Hussein Mohsen and Hasan Kurban and Mark Jenne and Mehmet Dalkilic",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2014 ; Conference date: 30-10-2014 Through 01-11-2014",
year = "2014",
month = mar,
day = "10",
doi = "10.1109/DSAA.2014.7058103",
language = "English",
series = "DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "399--405",
editor = "George Karypis and Longbing Cao and Wei Wang and Irwin King",
booktitle = "DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics",
address = "United States",
}