## 4 Results
### Accuracy results versus LIDC data subsets
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{6}{c}{**Cross-validation on**} & \multicolumn{4}{c}{**Validation of testing data**} \\ \multicolumn{2}{c}{**training data**} & & & \\ \hline
**DT** & **AD** & **DT** & **AD** & \(\#\) **of** & \(\#\) **of** & \(\#\) **of** \\  & & & & **Patients** & **Nodules** & **Instances** \\ \hline Lodulation & 49.39\% & 54.52\% & 18.60\% & 36.41\% & 87 & 19 & 209 \\ Malignancy & 39.44\% & 90.65\% & 31.00\% & 35.75\% & 84 & 19 & 213 \\ Margin & 38.54\% & 75.62\% & 36.11\% & 46.46\% & 97 & 22 & 217 \\ Sphericity & 33.89\% & 86.65\% & 14.26\% & 57.49\% & 84 & 19 & 226 \\ Spiculation & 60.24\% & 50.85\% & 33.53\% & 34.92\% & 84 & 19 & 237 \\ Subtlety & 38.87\% & 83.35\% & 25.14\% & 15.03\% & 82 & 18 & 248 \\ Texture & 67.26\% & 54.32\% & 40.88\% & 47.46\% & 95 & 21 & 193 \\
**Average** & **46.80\%** & **70.85\%** & **28.50\%** & **39.07\%** & **87** & **19** & **220** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Classification accuracies of Active-Decorate on original (90%) and reserved (10%) datasets. costs to different types of misclassifications (for example, misclassifying an instance as 3 when it is actually a 1 will receive a higher cost than when misclassifying it as 2 and a lower cost than when classifying it as 4), improves the results on the evaluation dataset by more than 20% (Table 9). This is done by the application of a cost matrix to the misclassification matrix before evaluating accuracy. In our case, we used the following cost matrix:  
Furthermore, we investigated the influence of the type of classifier on the accuracy of single classifiers and our proposed ensemble of classifiers approach. Tables 10 and 11 show how single classifiers compare to ensembles, for both decision trees and support vector machines (In the case of the ensembles, decision trees and support vector machines serve as the base classifier). In average, the performance of an ensemble always exceeds the performance of a single classifier, and the performance of the support vector machine almost always exceeds the performance of the decision tree. In particular, the support vector machine does better on the reserved data set, meaning the support vector machine generalizes better than the decision tree. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**DT**} & \multicolumn{4}{c}{**DT ensemble**} \\ \cline{2-7}  & **full** & **original** & **reserved** & **full** & **original** & **reserved** \\ \hline Lobulation & 49.40\% & 49.39\% & 18.60\% & 54.53\% & 54.52\% & 36.41\% \\ Malignancy & 39.11\% & 39.44\% & 31.00\% & 89.89\% & 90.65\% & 35.75\% \\ Margin & 38.56\% & 38.54\% & 36.11\% & 75.67\% & 75.62\% & 46.46\% \\ Sphericity & 34.21\% & 33.89\% & 14.26\% & 87.47\% & 86.65\% & 57.49\% \\ Spiculation & 59.43\% & 60.24\% & 33.53\% & 50.17\% & 50.85\% & 34.92\% \\ Subtlety & 38.11\% & 38.87\% & 25.14\% & 81.73\% & 83.35\% & 15.03\% \\ Texture & 66.74\% & 67.26\% & 40.88\% & 53.90\% & 54.32\% & 47.46\% \\
**Average** & **46.51\%** & **46.80\%** & **28.50\%** & **70.48\%** & **70.85\%** & **39.07\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Classification Accuracy of decision trees on full, original and reserved data sets (single classifier _vs._ ensemble of classifiers). \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **AD (original** & **AD (original data)** & **AD (reserved** & **AD (reserved data)** \\  & **data)** & **after applying cost** & **data)** & **after applying costs** \\ \hline Lobulation & 54.52\% & 67.99\% & 36.41\% & 61.48\% \\ Malignancy & 90.65\% & 93.65\% & 35.75\% & 62.91\% \\ Margin & 75.62\% & 84.75\% & 46.46\% & 70.74\% \\ Sphericity & 86.65\% & 90.60\% & 57.49\% & 75.44\% \\ Spiculation & 50.85\% & 58.95\% & 34.92\% & 49.79\% \\ Subtlety & 83.35\% & 89.37\% & 15.03\% & 51.01\% \\ Texture & 54.32\% & 70.51\% & 47.46\% & 63.47\% \\
**Average** & **70.85\%** & **79.40\%** & **39.07\%** & **62.12\%** \\ \hline \hline \end{tabular}
\end{table}