# Dependence Representation Learning with Convolutional Neural Networks and 2D Histograms
## 4 Feature Generation
### Results  
Table 5 lists the average classification accuracy for all of the test datasets. Our method outperforms AutoLearn with respect to the average classification accuracy of the five classification algorithms except for KNN and LR algorithms. Table 6 lists the total numbers of features generated by each method for the 13 test datasets. AutoLearn generated new features for 88% of the original feature pairs, whereas, our method generated new features for 47% of the original feature pairs. Note that even though our method generated half as many features, it achieved higher classification accuracy than AutoLearn. This suggests that the proposed method produces much better quality features than AutoLearn. Tables 7-9 list detailed scores for each dataset. In terms of the average accuracy, our method is superior for all datasets except for the 'Arcene' dataset. Moreover, in the tables, the 'original' column represents the accuracy of original machine learning algorithm without feature generation. In summary, our method tends to have a relatively high classification accuracy in spite of the fact that the number of generated features is almost half the number of features generated by AutoLearn. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Methods** & **Original Features** & **Generated Features** \\ \hline AutoLearn & 7492 (12\%) & 54,506 (88\%) \\ Proposed & 32,806 (53\%) & 29,192 (47\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The total number of features generated by each method for the 13 test datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Classifier** & **Original** & **AutoLearn** & **Proposed** \\ \hline \multirow{4}{*}{Diabetes} & Average & 76.62 & 80.33 & **81.08** \\ \cline{2-4}  & KNN & 83.12 & 81.82 & **85.71** \\ \cline{2-4}  & LR & 84.42 & 84.42 & **85.71** \\ \cline{2-4}  & SVM-L & **83.12** & **83.12** & 81.82 \\ \cline{2-4}  & SVM-P & 81.82 & **83.12** & 83.12 \\ \cline{2-4}  & RF & 76.62 & **80.52** & 79.22 \\ \cline{2-4}  & AB & 77.92 & **79.22** & 76.62 \\ \cline{2-4}  & DT & **76.62** & 70.13 & 75.32 \\ \hline \hline \multirow{4}{*}{E.coli} & Average & 88.24 & 89.50 & **90.76** \\ \cline{2-4}  & KNN & 91.18 & 94.12 & **97.06** \\ \cline{1-1} \cline{2-4}  & LR & 94.12 & **97.06** & 94.12 \\ \cline{1-1} \cline{2-4}  & SVM-L & **97.06** & 94.12 & **97.06** \\ \cline{1-1} \cline{2-4}  & SVM-P & 91.18 & **94.12** & 91.18 \\ \cline{1-1} \cline{2-4}  & RF & 88.24 & 88.24 & **97.06** \\ \cline{1-1} \cline{2-4}  & AB & 67.65 & **73.53** & **73.53** \\ \cline{1-1} \cline{2-4}  & DT & **88.24** & 85.29 & 85.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison in terms of classification accuracy (1/3). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifier** & **Conventional** & **AutoLearn** & **Proposed** \\ \hline
**Average** & **0.7836** & **0.8014** & **0.8229** \\ \hline KNN & 0.7991 & **0.8275** & 0.8145 \\ LR & 0.8019 & **0.8210** & 0.8162 \\ SVM-L & 0.7750 & 0.7938 & **0.8022** \\ SVM-P & 0.8024 & 0.8022 & **0.8171** \\ RF & 0.7774 & 0.7547 & **0.7806** \\ AB & 0.7774 & 0.7701 & **0.7884** \\ DT & 0.7851 & 0.7958 & **0.8060** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average classification accuracy for 13 test datasets.