Breast Cancer Detection Using Convoluted Features and Ensemble Machine Learning Algorithm

. 2022 Dec 6;14(23):6015. doi: 10.3390/cancers14236015

Algorithm 1 Ensembling of LR and SGD.

Input: input data ${(x, y)}_{i = 1}^{N}$

$M_{L R}$ = Trained_ LR

$M_{S G D}$ = Trained_ SGD

1:
for $i = 1 t o M$ do
2:
if $M_{L R} \neq 0 & M_{S G D} \neq 0 & t r a i n i n g_s e t \neq 0$ then
3:
$P r o b S G D - 1 = M_{S G D} . p r o b a b i l i t y (1 - c l a s s)$
4:
$P r o b S G D - 2 = M_{S G D} . p r o b a b i l i t y (2 - c l a s s)$
5:
$P r o b L R - 1 = M_{R F} . p r o b a b i l i t y (1 - c l a s s)$
6:
$P r o b L R - 2 = M_{R F} . p r o b a b i l i t y (2 - c l a s s)$
7:
Decision function = $m a x (\frac{1}{N_{c l a s s i f i e r}} \sum_{c l a s s i f i e r}$

$(A v g_{(P r o b S G D - 1 P r o b L R - 1)}$

$, (A v g_{(P r o b S G D - 2, P r o b L R - 2)}$
8:
end if
9:
Return final label $\hat{p}$
10:
end for