# Long Length Document Classification by Local Convolutional Feature Aggregation
## 4 Experiment Analysis
### Experiment Results
#### 4.3.5 Experimental Comparison  
We compared the experimental results of the three proposed models and the baseline model. The comparison is shown in Figure 5. We can clearly see that the results of our proposed models are \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Total Words** & **Window Size** & **10** & **20** & **50** & **100** & **200** & **400** & **500** \\ \hline
200 & 89.84\% & 90.15\% & 91.67\% & 91.89\% & 92.23\% & & \\ \hline
400 & 90.05\% & 92.22\% & 93.08\% & 93.56\% & 93.67\% & 93.53\% & \\ \hline
600 & 90.11\% & 92.38\% & 93.68\% & 94.24\% & 94.11\% & & \\ \hline
800 & 92.36\% & 92.53\% & 93.86\% & 94.36\% & 94.41\% & 93.87\% & \\ \hline
1000 & 93.17\% & 93.21\% & 94.56\% & 94.68\% & 95.48\% & 94.73\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model CNN_RAM_Agg experimental results. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Total Words** & **Window Size** & **10** & **20** & **50** & **100** & **200** & **400** & **500** \\ \hline
200 & 88.16\% & 88.21\%\% & 90.67\% & 90.96\% & 91.32\% & & \\ \cline{2-8}  & 83.42\% & 86.05\% & 86.71\% & 87.23\% & 88.06\% & & \\ \hline
400 & 89.05\% & 91.03\% & 91.35\% & 91.62\% & 92.21\% & 92.33\% & \\ \cline{2-8}  & 86.28\% & 88.41\% & 89.08\% & 89.27\% & 90.08\% & 90.12\% & \\ \hline
600 & 89.52\% & 91.96\% & 92.03\% & 92.85\% & 93.87\% & & \\ \cline{2-8}  & 86.66\% & 89.59\% & 89.92\% & 90.23\% & 90.51\% & & \\ \hline
800 & 90.28\% & 92.41\% & 93.12\% & 93.32\% & 94.01\% & 94.35\% & \\ \cline{2-8}  & 89.44\% & 90.28\% & 90.37\% & 90.56\% & 90.82\% & 90.94\% & \\ \hline
1000 & 92.36\% & 92.85\% & 93.23\% & 93.86\% & 94.12\% & & 94.25\% \\ \cline{2-8}  & 89.48\% & 90.36\% & 90.61\% & 91.77\% & 91.96\% & & 92.34\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model CNN_LSTM_Agg experimental results. superior to the baseline with proper setting. In Figure 5a, because the baseline model only exploited the first 1000 words in a document, for fair comparison, we fixed the number of extracted words to 1000 and varied the window size for the three proposed models. Both CNN_LSTM_Agg and CNN_RAM_Agg outperform the baseline by a large margin for all windows size settings. CNN_Random_Agg is also superior to the baseline except that the window size was set to 10. Moreover, in Figure 5b, we can observe that even with much fewer extracted words, the proposed sub-sampling methods can beat the baseline. In particular, CNN_RAM_AGG outperforms the baseline with less than 400 words. It is not surprising that the sub-sampling methods are superior to the classic CNN baseline, because the CNN baseline is limited to the first 1000 words because of memory constraints. On the contrary, these sub-sampling based methods make use of the full coverage. To investigate the performance of these sub-sampling approaches, we first varied the window size from 10 to 500 while fixing the total number of words to 1000. The results are shown in Figure 5a. It can be observed that the classification accuracy of the three models gradually increases as the size of the window increases. However, for CNN_RAM_Agg, there is an interesting decrease when setting window size from 200 to 500. This is because the total number of extracted words is only 1000, then the window size of 500 means CNN_RAM_Agg can only glimpse two word blocks, but for the window size of 200 CNN_RAM_Agg, can glimpse five word blocks. In some sense, more word blocks may cover more global information, which increases the possibility of it focusing on other salient parts. Secondly, by fixing the window size to 40, we increased the total number of extracted words from 200 to 4000. Note that for CNN_RAM_Agg, we only extracted up to 1000 words. Figure 5b demonstrates that CNN_RAM_Agg significantly outperforms the other two models with the least information. Even with 200 extracted words, say only 3% of the average words are used, CNN_RAM_Agg can achieve more than 90% classification accuracy.