# The Effect of Preprocessing on Arabic Document Categorization
## 5 Experiment and Results
### Experimental Results and Analysis
The amount of variation in accuracies proves that the appropriate combinations of preprocessing tasks depending on the classification algorithm and feature size may significantly improve the accuracy. On the contrary, inappropriate combinations of preprocessing tasks may significantly reduce the accuracy. The impact of preprocessing on Arabic document categorization was also statistically analyzed using a one-way analysis of variance (ANOVA) over the maximum and minimum Micro-F1 at each feature size with an alpha value = 0.05. \(p\)-values were obtained as 0.001258, 0.004316, and 0.203711 for NB, SVM, and KNN, respectively. The results have supported our hypothesis that performance differences are statistically significant with a significance level of 0.05. We also compared the preprocessing combinations, which provided the maximum accuracy for each classification technique, and the preprocessing combinations, which provided the maximum accuracy at minimum feature size for each classification technique. As shown in Table 8, normalization and stemming should be applied to achieve either maximum accuracy or minimum feature size with the maximum accuracy for KNN and SVM classifiers regardless of the feature size. On the contrary, the statuses of preprocessing tasks were opposite for the NB classifier. Furthermore, the findings of the proposed study in terms of contribution to the accuracy were compared against the previous works mentioned in the related works section. The comparison is presented in Table 9, where \"+\" and \"\(-\)\" signs, respectively, represent positive and negative effects, and \"N\(/\)F\" indicate that the corresponding analysis is not found. In general, the results clearly showed the superiority of the SVM over the NB and KNN algorithms for all experiments, especially when the feature size increases. Similarly, [PERSON] [39] compared the performance of KNN and SVM algorithms for Arabic document categorization and concluded that SVM outperformed KNN and showed a better micro average F1 and prediction time. SVM algorithm is superior because this technique has the ability to handle high dimensional data. Furthermore, this \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification** & \multicolumn{4}{c}{**Preprocessing Tasks (Max. Accuracy)**} & \multicolumn{2}{c}{**Preprocessing Tasks (Min. Feature Size)**} \\ \cline{2-6}
**Technique** & **Feature Size** & **Max. Micro-F1** & **Preprocessing Tasks** & **Feature Size** & **Max. Micro-F1** & **Preprocessing Tasks** \\ \hline Naive Bayes & 2000 & 0.9319 & NR: 0 \(\mid\) SR: 1 \(\mid\) IS: 0 & 50 & 0.7326 & NR: 1 \(\mid\) SR: 0 \(\mid\) LS: 1 \\ KNN & 500 & 0.8537 & NR: 1 \(\mid\) SR: 0 \(\mid\) IS: 1 & 50 & 0.7648 & NR: 1 \(\mid\) SR: 0 \(\mid\) LS: 1 \\ SVM & 2000 & 0.9674 & NR: 1 \(\mid\) SR: 0 \(\mid\) IS: 1 & 50 & 0.8030 & NR: 1 \(\mid\) SR: 0 \(\mid\) LS: 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Preprocessing tasks (maximum accuracy _vs._ minimum feature size). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **NR** & **SR** & **LS** \\ \hline [PERSON] _et al._[7] & N/F & - & + (often) \\ [PERSON] _et al._[6] & N/F & + & + \\ [PERSON] _et al._[8] & N/F & - & - \\ [PERSON] _et al._[9] & N/F & N/F & N/F \\ [PERSON] _et al._[4] & N/F & + & + \\ [PERSON] _et al._[5] & N/F & + & - \\ [PERSON] [10, 11] & N/F & N/F & - \\ [PERSON] _et al._[15] & N/F & N/F & + \\ [PERSON] [14] 2015 & N/F & N/F & + \\ [PERSON] _et al._[18] & N/F & N/F & + \\ [PERSON] _et al._[12] & N/F & + & N/F \\ [PERSON] _et al._[16] & N/F & N/F & N/F \\ [PERSON] _et al._[17] & N/F & N/F & - \\ \hline
**Proposed Study** & **+** & **- (often)** & **+ (often)** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The comparison of the influence of the preprocessing task in various studies and proposed study. algorithm can easily test the effect of the number of features on classification accuracy, which ensures robustness to different dataset and preprocessing tasks. By contrast, the KNN performance degrades when the feature size increases.