# Simultaneous Instance and Attribute Selection for Noise Filtering
## 4 Discussion
In addition, using heuristics does not provide evidence of obtaining highly accurate preprocessed training sets. Regarding instance retention, the best algorithms were ROFS (first in [PERSON]'s ranking for 0% and 15% and second in the ranking for 5% and 10%) and DS (first in [PERSON]'s ranking for 10% and 5% and second in the ranking for 0% and 15%). There were no statistically significant differences between ROFS and DS in any of the four analyzed scenarios. For noise-free and 5% noise, all remaining algorithms kept a significantly larger number of instances. However, for the 10% and 15% noise scenarios, the IN-GA algorithm did not show statistically significant differences with respect to the first-ranked algorithm. Unfortunately, it was significantly less accurate, as shown in Table 6. All remaining algorithms kept a significantly larger number of instances than the first-ranked algorithm for 10% and 15% noise. The statistical analysis regarding instance selection concludes that using a combination of an error-based editing method followed by a condensation method leads to results retaining significantly fewer instances than using evolutive approaches or only condensation approaches. Regarding feature retention, the evolutive algorithms had the best performance, followed by ROFS and without statistically significant differences between them. The remaining algorithms kept a significantly larger number of features in all scenarios. TCCS and SOFSA do not include strategies to reduce features besides using typical testors, and DS uses SBS that starts with all features and then tries to reduce them. These results show that evolutionary strategies lead to preprocessed data that retain significantly fewer features than using typical testors, wrapper feature selection algorithms, \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**0\% Noise**} & \multicolumn{2}{c}{**5\% Noise**} & \multicolumn{2}{c}{**10\% Noise**} & \multicolumn{2}{c}{**15\% Noise**} \\ \cline{2-9}  & **[PERSON]’s** & **Holm’s** & **[PERSON]’s** & **Holm’s** & **[PERSON]’s** & **Holm’s** & **[PERSON]’s** & **Holm’s** \\  & **Ranking** & _p-Value_ & **Ranking** & _p-Value_ & **Ranking** & _p-Value_ & **Ranking** & _p-Value_ \\ \hline ROFS & 1.467 & - & 1.667 & 0.947 & 1.667 & 0.947 & 1.600 & - \\ DS & 1.533 & 0.947 & 1.600 & - & 1.600 & - & 1.667 & 0.947 \\ IN-GA & 4.000 & _0.011_ & 3.933 & _0.020_ & 3.533 & 0.053 & 3.667 & 0.039 \\ KJ-GA & 4.667 & _0.001_ & 4.600 & _0.003_ & 4.400 & _0.005_ & 4.200 & _0.009_ \\ RMHC-FP1 & 5.400 & _0.000_ & 5.400 & _0.000_ & 5.200 & _0.000_ & 5.067 & _0.001_ \\ AKH-GA & 5.867 & _0.000_ & 5.933 & _0.000_ & 5.400 & _0.000_ & 5.600 & _0.000_ \\ TCCS & 6.033 & _0.000_ & 6.233 & _0.000_ & 6.933 & _0.000_ & 6.967 & _0.000_ \\ SOFSA & 7.033 & _0.000_ & 6.633 & _0.000_ & 7.267 & _0.000_ & 7.233 & _0.000_ \\ Baseline & 9.000 & _0.000_ & 9.000 & _0.000_ & 9.000 & _0.000_ & 9.000 & _0.000_ \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Friedman + Holm results, for instance, retention of 1-NN. Rejected hypotheses are in italics.