1. // initialization 2. $W_0 \leftarrow (w_1, \dots, w_m) \leftarrow 1/m$ 3. $W \leftarrow W_0$ 4. $Vars \leftarrow {\cal{X}}$ 5. $\delta \leftarrow 10^{-4}$ 6. $convergence \leftarrow True$ 7. $maxTolerancia \leftarrow 3$ 8. $bisection \leftarrow False$ 9. $error \leftarrow \inf$ 10. $finished \leftarrow False$ 11. $AODE \leftarrow \emptyset$ // the ensemble 12. $tolerance \leftarrow 0$ 13. $numModelsInPack \leftarrow 0$ 15. // main loop 16. While (!finished) 1. $\pi \leftarrow SortFeatures(Vars, criterio, D[W])$ 2. $k \leftarrow 2^{tolerance}$ 3. if ($tolerance == 0$) $numItemsPack \leftarrow0$ 4. $P \leftarrow Head(\pi,k)$ // first k features in order 6. $i \leftarrow 0$ 7. While ($i < size(P)$) 1. $X \leftarrow P[i]$ 2. $i \leftarrow i + 1$ 3. $numItemsPack \leftarrow numItemsPack + 1$ 4. $Vars.remove(X)$ 5. $spode \leftarrow BuildSpode(X, {\cal{X}}, D[W])$ 6. $\hat{y}[] \leftarrow spode.Predict(D[W])$ 7. $\epsilon \leftarrow error(\hat{y}[], y[])$ 8. $\alpha \leftarrow \frac{1}{2} ln \left ( \frac{1-\epsilon}{\epsilon} \right )$ 9. if ($\epsilon > 0.5$) 1. $finished \leftarrow True$ 2. break 10. $AODE.add( (spode,\alpha_t) )$ 11. $W \leftarrow UpdateWeights(D[W],\alpha,y[],\hat{y}[])$ 8. if ($convergence$ $\And$ $! finished$) 1. $\hat{y}[] \leftarrow AODE.Predict(D[W])$ 2. $e \leftarrow error(\hat{y}[], y[])$ 3. if $(e > (error+\delta))$ // result doesn't improve 1. if $(tolerance == maxTolerance)\; finished\leftarrow True$ 2. else $tolerance \leftarrow tolerance+1$ 4. else 1. $tolerance \leftarrow 0$ 2. $error \leftarrow min(error,e)$ 9. if $(Vars == \emptyset) \; finished \leftarrow True$ 17. if ($tolerance == maxTolerance$) // algorithm finished because of lack of convergence 1. $removeModels(AODE, numItemsPack)$ 2. $W \leftarrow W_B$ 18. Return $AODE$