# On a Hybridization of Deep Learning and Rough Set Based Granular Computing
## 4 Procedure for Performed Experiments
### The Results of Experiments
Figure 2:1 Dummy variables - mean result for 10 learning cycles, using 10 splits; for Australian credit data set; after conversion to Dummy variables its 35 attributes. Figure 2:2 Results for 10 learning cycles, using 10 splits; for Australian credit data set converted to dummy variables (after conversion to dummy variables its 35 attributes); In ‘percentage of objects’ ax, we have the percentage size of granulated data vs accuracy of classification in ‘Accuracy’ ax; in. The results are not perfectly evenly matched or at the same points on the x-axis, due to the fact that the size reduction levels of the training systems varied. \begin{table}
\begin{tabular}{l c c c c} \hline  & **no\_of\_gran\_objects** & **percentage\_of\_objects** & **time\_to\_learn** & **accuracy** \\  & **Mean** & **Mean** & **Mean** & **Mean** \\
**gran\_rad** & & & & \\ \hline
0.0714 & 2.0 & 0.9434 & 0.3702 & 0.6801 \\
0.1429 & 2.3 & 1.0849 & 0.3786 & 0.6505 \\
0.2143 & 2.8 & 1.3208 & 0.3708 & 0.6231 \\
0.2857 & 4.5 & 2.1226 & 0.3758 & 0.7132 \\
0.3571 & 8.8 & 4.1509 & 0.3928 & 0.7110 \\
0.4286 & 17.0 & 8.0189 & 0.4064 & 0.7187 \\
0.5000 & 35.7 & 16.8396 & 0.4209 & 0.7198 \\
0.5714 & 70.4 & 33.2075 & 0.4299 & 0.7560 \\
0.6429 & 122.7 & 57.8774 & 0.4571 & 0.7813 \\
0.7143 & 177.9 & 83.9151 & 0.4872 & 0.8231 \\
0.7857 & 204.6 & 96.5094 & 0.4942 & 0.8297 \\
0.8571 & 211.4 & 99.7170 & 0.4995 & 0.8220 \\
0.9286 & 211.4 & 99.7170 & 0.5023 & 0.8209 \\
1.0000 & 211.4 & 99.7170 & 0.5010 & 0.8253 \\ \hline \end{tabular}
\end{table} 
Table 6: Results for Heart disease dataset (mean from 10 experiments). \begin{table}
\begin{tabular}{l c c c c} \hline  & **no\_of\_gran\_objects** & **percentage\_of\_objects** & **time\_to\_learn** & **accuracy** \\  & **Mean** & **Mean** & **Mean** & **Mean** \\
**gran\_rad** & & & & \\ \hline
0.0667 & 2.0 & 0.4149 & 0.3666 & 0.5646 \\
0.1333 & 2.0 & 0.4149 & 0.3607 & 0.5337 \\
0.2000 & 3.4 & 0.7054 & 0.3691 & 0.5423 \\
0.2667 & 5.1 & 1.0581 & 0.3685 & 0.5154 \\
0.3333 & 8.2 & 1.7012 & 0.3696 & 0.5192 \\
0.4000 & 16.0 & 3.3195 & 0.3778 & 0.5577 \\
0.4667 & 31.6 & 6.5560 & 0.3777 & 0.6236 \\
0.5333 & 65.3 & 13.5477 & 0.3916 & 0.7764 \\
0.6000 & 145.3 & 30.1452 & 0.4287 & 0.8125 \\
0.6667 & 283.8 & 58.8797 & 0.7464 & 0.8399 \\
0.7333 & 412.9 & 85.6639 & 0.8210 & 0.8534 \\
0.8000 & 468.8 & 97.2614 & 0.8585 & 0.8587 \\
0.8667 & 477.9 & 99.1494 & 0.8532 & 0.8553 \\
0.9333 & 479.3 & 99.4398 & 0.8817 & 0.8553 \\
1.0000 & 482.0 & 100.0000 & 0.8995 & 0.8562 \\ \hline \end{tabular}
\end{table} 
Table 5: Results for Australian credit dataset (mean from 10 experiments). When considering the results for Heart Disease data set (see Figures 5 and 6 and Table 6), for a radius of 0.756, with a reduction in the number of training objects of up to 67 percent, we get an accuracy of 0.756 compared to the original system of 0.825. For a radius of 0.643, with a reduction of nearly 42 percent, we get an accuracy of 0.781, while for a radius of 0.714, where granulation reduces 16 percent of the objects, we get a 0.823. In the case of results for the Australian Credit system (see Figures 3 and 4 and Table 5), within a radius of 0.6 we get a reduction of about 70 percent and a classification accuracy of 0.813 compared to 856 percent on the original system. In the case of 0.667 radius, with a reduction of 41 per cent, we get an accuracy of 0.84. In case of radius 0.733, with reduction in training system size in the range od 14 percent, we have accuracy 0.853. \begin{table}
\begin{tabular}{l c c c c} \hline  & **no\_of\_gran\_objects** & **percentage\_of\_objects** & **time\_to\_learn** & **accuracy** \\  & **Mean** & **Mean** & **Mean** & **Mean** \\
**gran\_rad** & & & & \\ \hline  
In our next experiment for the Pima Indians Diabetes system (see results in Figures 7 and 8 and Table 7), for a radius of 0.333 and a reduction in the number of objects of about 73 percent, we get an accuracy of 0.7 compared to 0.772 on non-granular data.