Optimizing Computer Worm Detection Using Ensembles

Algorithm 5

Gradient boosting.

Inputs:

(i) input data (x, y) Ni=1

(ii) number of iterations M

(iii) choice of the loss-function (y, f)

(iv) choice of the base-learner model h (x, θ)

Algorithm:

(1) initialize f₀ with a constant

(2) for t = 1 to M do

(3) compute the negative gradient gt(x)

(4) fit a new base-learner function h(x, θt)

(5) find the best gradient descent step-size ρt  : ρt = arg

min ρ N i=1 yi, ft−1(xi) + ρh(xi, θt)

(6) update the function estimate: ft ← ft−1 + ρth(x, θt)

(7) end for