## 3 Methods
### Char-Grams Models
SMO, however, achieves better accuracy only combining char-grams of five-, six and seven-char-grams, but the difference with the full combination of char-grams is minimal. If we compare the results with the use of individual fixed-length, there are no improvement so we can conclude that the addition of more combinations of char-grams can only produce an overhead for the machine-learning classifiers. Regarding the differences of the machine-learning classifiers, RF always achieves best results than SMO in all combinations. It draws our attention that the best result of SMO with combinations of (five, six, seven and eight) char-grams is worse than RF even with the double of the corpus size. Figures 12 and 13 shows how the accuracy varies among the corpus size, where we can observe that the behaviour of the classifiers gets similar with corpus-3, corpus-4 and corpus-5. \begin{table}
\begin{tabular}{l r r r r r r r r} \hline \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**Configations of Five-, Six- and Seven-Char-Grams**} \\ \hline  & \multicolumn{4}{c}{**RF**} & \multicolumn{4}{c}{**SMO**} \\ \hline
**Set** & **P** & **R** & **F1** & **ACC** & **P** & **R** & **F1** & **ACC** \\ \hline corpus-1 & 85.00 & 85.00 & 85.00 & 85.00 & 75.50 & 71.00 & 69.70 & 71.00 \\ corpus-2 & 88.60 & 88.00 & 88.00 & 88.00 & 82.70 & 82.50 & 82.50 & 82.50 \\ corpus-3 & 90.40 & 90.30 & 90.20 & **90.30** & 89.50 & 89.50 & 89.50 & **89.50** \\ corpus-4 & 89.80 & 89.80 & 89.70 & 89.80 & 89.10 & 89.10 & 89.10 \\ corpus-5 & 88.70 & 88.70 & 88.70 & 88.70 & 88.20 & 88.20 & 88.20 & 88.20 \\ corpus-6 & 86.70 & 86.60 & 86.60 & 86.60 & 86.50 & 86.50 & 86.50 & 86.50 \\ corpus-7 & 80.10 & 80.00 & 80.00 & 80.00 & 80.70 & 80.60 & 80.60 & 80.60 \\ \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**Combinations of Six-, Seven- and Eight-Char-Grams**} \\ \hline  & \multicolumn{4}{c}{**RF**} & \multicolumn{4}{c}{**SMO**} \\ \hline
**Set** & **P** & **R** & **F1** & **ACC** & **P** & **R** & **F1** & **ACC** \\ \hline corpus-1 & 81.00 & 81.00 & 81.00 & 81.00 & 70.90 & 69.00 & 68.30 & 69.00 \\ corpus-2 & 88.60 & 88.00 & 88.00 & 88.00 & 82.10 & 82.00 & 82.00 & 82.00 \\ corpus-3 & 89.10 & 89.00 & 89.00 & **89.00** & 88.00 & 88.00 & **88.00** & 88.00 \\ corpus-4 & 87.60 & 87.60 & 87.60 & 87.60 & 87.80 & 87.80 & 87.70 & 87.80 \\ corpus-5 & 86.60 & 86.60 & 86.60 & 86.60 & 87.30 & 87.30 & 87.30 & 87.30 \\ corpus-6 & 85.70 & 85.70 & 85.70 & 85.70 & 85.60 & 85.60 & 85.60 & 85.60 \\ corpus-7 & 77.80 & 77.70 & 77.60 & 77.70 & 79.20 & 79.10 & 79.00 & 79.10 \\ \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**Combinations of Five-, Six-, Seven- and Eight-Char-Grams**} \\ \hline  & \multicolumn{4}{c}{**RF**} & \multicolumn{4}{c}{**SMO**} \\ \hline
**Set** & **P** & **R** & **F1** & **ACC** & **P** & **R** & **F1** & **ACC** \\ \hline corpus-1 & 82.10 & 82.00 & 82.00 & 82.00 & 79.20 & 73.00 & 71.50 & 73.00 \\ corpus-2 & 89.00 & 88.50 & 88.50 & 88.50 & 82.50 & 82.00 & 81.90 & 82.00 \\ corpus-3 & 90.80 & 90.80 & 90.70 & **90.80** & 89.00 & 89.00 & 89.00 & 89.00 \\ corpus-4 & 89.40 & 89.40 & 89.40 & 89.40 & 89.30 & 89.30 & 89.20 & **89.30** \\ corpus-5 & 88.60 & 88.60 & 88.60 & 88.60 & 87.90 & 87.90 & 87.90 & 87.90 \\ corpus-6 & 87.00 & 86.90 & 86.90 & 86.90 & 86.30 & 86.30 & 86.30 & 86.30 \\ corpus-7 & 79.80 & 79.70 & 79.70 & 79.70 & 80.40 & 80.40 & 80.40 & 80.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results using triplets of fixed-length character for the char-gram model and by mixing all the fixed length character combinations.