On this article, Simon Bernard proposes a new approach for constructing Random Forest called Dynamic Random Forest. I am new on this subject, so after reading the article, I have a doubt regarding the algorithm about how he uses the weights. To follow notation:
Let $T=\{(x_1,y_1),\ldots,(x_N,y_N)\}$ the training set. I think the algorithm is as follows for $l=1$ and $l=2$.
- $l=1$: We obtain a sample of $N$ training samples from $T$ according to an uniform distribution, and we call it $T_1$. Then we obtain a sample of $N$ training instances from $T_1$ according to $D_1$ (in this case, it would also be uniform). We build the tree and perform the calculation below.
- $l=2$: We obtain a sample of $N$ training samples from $T$ according to an uniform distribution, and we call it $T_2$. Then we obtain a sample of $N$ training instances from $T_2$ according to $D_2$, which is no longer an uniform distribution.
Example:
Let $T=\{(x_1,y_1),\ldots,(x_5,y_5)\}$
$l=1$ $\longrightarrow$ $T_1=\{(x_2,y_2),(x_4,y_4),(x_2,y_2),(x_5,y_5),(x_4,y_4)\}$ and we use to build the tree (using $D_1$) $\{(x_4,y_4),(x_2,y_2),(x_2,y_2),(x_5,y_5),(x_2,y_2)\}$
$l=2$ $\longrightarrow$ $T_2=\{(x_3,y_3),(x_4,y_4),(x_1,y_1),(x_4,y_4),(x_3,y_3)\}$ and we use to build the tree (according to $D_2$) $\{(x_4,y_4),(x_1,y_1),(x_3,y_3),(x_3,y_3),(x_1,y_1)\}$
Questions:
Did I understand the algorithm correctly?
When you are finished, if we give the forest a new input $x$, how do we decide its class? By majority vote?
Article: Simon Bernard, Sébastien Adam, Laurent Heutte. Dynamic Random Forests. Pattern Recognition Letters, Elsevier, 2012, 33 (12), pp.1580-1586.