\section{Algorithm}
\begin{itemize}
\item[] // notation
\item $n$ features ${\cal{X}} = \{X_1, \dots, X_n\}$ and the class $Y$
\item $m$ instances. 
\item $D = \{ (x_1^i, \dots, x_n^i, y^i) \}_{i=1}^{m}$
\item $W$ a weights vector. $W_0$ are the initial weights.
\item $D[W]$ dataset with weights $W$ for the instances.
\end{itemize}
\bigskip


\begin{enumerate}
\item[] // initialization
\item $W_0 \leftarrow (w_1, \dots, w_m) \leftarrow 1/m$
\item $W \leftarrow W_0$
\item $Vars \leftarrow {\cal{X}}$
\item $\delta \leftarrow 10^{-4}$
\item $convergence \leftarrow True$ // hyperparameter
\item $maxTolerancia \leftarrow 3$ // hyperparameter
\item $bisection \leftarrow False$ // hyperparameter
\item $finished \leftarrow False$
\item $AODE \leftarrow \emptyset$ \hspace*{2cm} // the ensemble
\item $tolerance \leftarrow 0$
\item $numModelsInPack \leftarrow 0$
\item $maxAccuracy \leftarrow -1$
\item[] 
\newpage
\item[] // main loop
\item While $(\lnot finished)$
\begin{enumerate}
    \item $\pi \leftarrow SortFeatures(Vars, criterio, D[W])$
    \item $k \leftarrow 2^{tolerance}$
    \item if ($tolerance == 0$) $numItemsPack \leftarrow0$
    \item $P \leftarrow Head(\pi,k)$ \hspace*{2cm} //  first k features in order
    \item $spodes \leftarrow \emptyset$
    \item $i \leftarrow 0$
    \item While ($ i < size(P)$)
    \begin{enumerate}
        \item $X \leftarrow P[i]$
        \item $i \leftarrow i + 1$
        \item $numItemsPack \leftarrow numItemsPack + 1$
        \item $Vars.remove(X)$
        \item $spode \leftarrow BuildSpode(X, {\cal{X}}, D[W])$
        \item $\hat{y}[] \leftarrow spode.Predict(D[W])$
        \item $\epsilon \leftarrow error(\hat{y}[], y[])$
        \item $\alpha \leftarrow \frac{1}{2} ln \left ( \frac{1-\epsilon}{\epsilon} \right )$
        \item if ($\epsilon > 0.5$)
        \begin{enumerate}
            \item $finished \leftarrow True$
            \item break
        \end{enumerate}
        \item $spodes.add( (spode,\alpha_t) )$
        \item $W \leftarrow UpdateWeights(D[W],\alpha,y[],\hat{y}[])$
    \end{enumerate}       
    \item $AODE.add( spodes )$
    \item if ($convergence \land \lnot finished$) 
    \begin{enumerate}       
        \item $\hat{y}[] \leftarrow AODE.Predict(D[W])$
        \item $actualAccuracy \leftarrow accuracy(\hat{y}[], y[])$
        \item $if (maxAccuracy == -1)\; maxAccuracy \leftarrow actualAccuracy$
        \item if $((accuracy - maxAccuracy) < \delta)$\hspace*{2cm} // result doesn't improve enough
        \begin{enumerate}
            \item $tolerance \leftarrow tolerance + 1$
        \end{enumerate}
        \item else
        \begin{enumerate}
            \item $tolerance \leftarrow 0$
            \item $numItemsPack \leftarrow 0$
         \end{enumerate}
    \end{enumerate}
    \item If $(Vars == \emptyset \lor tolerance>maxTolerance) \; finished \leftarrow True$
    \item $lastAccuracy \leftarrow max(lastAccuracy, actualAccuracy)$
\end{enumerate}
\item if ($tolerance > maxTolerance$) \hspace*{1cm} // algorithm finished because of lack of convergence
\begin{enumerate}
    \item $removeModels(AODE, numItemsPack)$
\end{enumerate}
\item Return $AODE$
\end{enumerate}