From de15bfa20b20c2818cffedce9174867852e86f68 Mon Sep 17 00:00:00 2001
From: Charly Lamothe <charly.lamothe@sfr.fr>
Date: Wed, 9 Oct 2019 02:27:10 +0200
Subject: [PATCH] Add Fawagreh2015 desc
---
reports/bolsonaro.tex | 2 ++
reports/bolsonaro_biblio.bib | 18 +++++++++++++++++-
2 files changed, 19 insertions(+), 1 deletion(-)
diff --git a/reports/bolsonaro.tex b/reports/bolsonaro.tex
index 04b5827..9c83998 100644
--- a/reports/bolsonaro.tex
+++ b/reports/bolsonaro.tex
@@ -75,6 +75,8 @@ where: $cor_{t_i, t_j} = correltion(predict_{t_i}, predict_{t_j} ) $ is the corr
\item $measure_3$
\end{itemize}
For the experiments, they use breast cancer prognosis. They reduce the size of a forest of 100 trees to a forest of on average 26 trees keeping the same error rate.
+
+\item \cite{Fawagreh2015}: The goal is to get a much smaller forest while staying accurate and diverse. To do so, they used a clustering algorithm. Let $C(t_i, T) = \{c_{i1}, \dots, c_{im}\}$ denotes a vector of class labels obtained after having $t_i$ classify the training set $T$ of size $m$, with $t_i \in F$, $F$ the forest of size $n$. Let $\mathcal{C} = \bigcup^n_{i=1} C(t_i, T)$ be the super vector of all class vectors classified by each tree $t_i$. They then applied a clustering algorithm on $\mathcal{C}$ to find $k = \sqrt{\frac{n}{2}}$ clusters. Finally, the final forest $F'$ is composed on the union of each tree that is the most representative per cluster, for each cluster. So if you have 100 trees and 7 clusters, the final number of trees will be 7. They obtained at least similar performances as with regular RF algorithm.
\end{itemize}
diff --git a/reports/bolsonaro_biblio.bib b/reports/bolsonaro_biblio.bib
index b4e1683..94efbec 100644
--- a/reports/bolsonaro_biblio.bib
+++ b/reports/bolsonaro_biblio.bib
@@ -11,4 +11,20 @@
@article{Zhang,
title={Search for the smallest random forest},
author={Zhang, Heping and Wang, Minghui}
-}
\ No newline at end of file
+}
+
+@article{Fawagreh2015,
+ author = {{Fawagreh}, Khaled and {Medhat Gaber}, Mohamad and {Elyan}, Eyad},
+ title = "{On Extreme Pruning of Random Forest Ensembles for Real-time Predictive Applications}",
+ journal = {arXiv e-prints},
+ keywords = {Computer Science - Machine Learning},
+ year = "2015",
+ month = "Mar",
+ eid = {arXiv:1503.04996},
+ pages = {arXiv:1503.04996},
+archivePrefix = {arXiv},
+ eprint = {1503.04996},
+ primaryClass = {cs.LG},
+ adsurl = {https://ui.adsabs.harvard.edu/abs/2015arXiv150304996F},
+ adsnote = {Provided by the SAO/NASA Astrophysics Data System}
+}
--
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