## 4 Results
### Accuracy results versus LIDC data subsets
\begin{table}
\begin{tabular}{l l l l l l l l l l l l l} \hline \hline \multicolumn{1}{c}{**Dataset**} & \multicolumn{3}{c}{**LIDC**} & \multicolumn{3}{c}{**LIDC85**} & \multicolumn{3}{c}{**LIDC\_B**} \\ \hline \multirow{3}{*}{**Characteristics**} & **L/U** & **\#of** & **N** & **C** & **L/U** & **\#of** & **N** & **C** & **L/U** & **\#of** & **N** & **C** \\  & **ratio** & **ITI** & & & & **ratio** & **ITI** & & & **ratio** & **ITI** & \\ \hline Lobulation & 0.51 & 748 & 197 & 99 & 0.20 & 63 & 21 & 19 & 0.34 & 266 & 57 & 31 \\ Malignancy & 0.30 & 503 & 133 & 73 & 0.19 & 61 & 22 & 17 & 0.23 & 503 & 121 & 67 \\ Margin & 0.35 & 570 & 148 & 84 & 0.17 & 56 & 19 & 14 & 0.29 & 365 & 126 & 79 \\ Sphericity & 0.30 & 516 & 135 & 80 & 0.29 & 85 & 27 & 20 & 0.23 & 477 & 131 & 77 \\ Spiculation & 0.68 & 893 & 236 & 120 & 0.30 & 87 & 28 & 24 & 0.17 & 192 & 63 & 52 \\ Subtlety & 0.31 & 519 & 137 & 87 & 0.30 & 88 & 27 & 22 & 0.23 & 296 & 77 & 46 \\ Texture & 0.89 & 1040 & 277 & 123 & 0.46 & 120 & 35 & 24 & 0.23 & 173 & 36 & 11 \\
**Average** & **0.45** & **684** & **180** & **95** & **0.26** & **80** & **25** & **20** & **0.25** & **324** & **87** & **51** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Structure of the initial training data for all three datasets; L/U ratio represents the ratio between the labeled versus unlabeled data; ITI stands for initial training instances, N for the number of nodules and C for the number of cases. levels of agreement and the distribution of semantic ratings. Further, if a patient had multiple nodules they were all included in the reserved 10%. Not surprisingly, when tested on a set of data that had never been viewed, both the single decision tree and our ensemble produced lower accuracies. However, one of the main features of the Active-DECORATE algorithm is its ability to dynamically adjust the ensemble when fed with newly available instances. In other words, the ensemble will not be generated just once, and then used in immutable form for classification of every new instance, but rather learn from every new instance it classifies, every time modifying the classification rules accordingly. Furthermore, associating different \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{6}{c}{**Decision trees**} & \multicolumn{4}{c}{**Ensemble approach**} \\ \hline
**Characteristics** & **LIDC** & **LIDC85** & **LIDC\_B** & **LIDC** & **LIDC85** & **LIDC\_B** \\ \hline Lodulation & 49.4\% & 27.44\% & 38.52\% & 54.53\% & 81.00\% & 83.56\% \\ Malignancy & 39.11\% & 42.22\% & 38.88\% & 89.89\% & 96.31\% & 89.38\% \\ Margin & 38.56\% & 35.36\% & 39.56\% & 75.67\% & 98.68\% & 93.58\% \\ Sphericity & 34.21\% & 36.15\% & 32.21\% & 87.47\% & 91.03\% & 85.86\% \\ Spiculation & 59.43\% & 36.15\% & 59.16\% & 50.17\% & 63.06\% & 82.5\% \\ Subtlety & 38.11\% & 38.79\% & 39.51\% & 81.73\% & 93.14\% & 93.93\% \\ Texture & 66.74 & 53.56\% & 60.42\% & 53.9\% & 97.10\% & 95.83\% \\
**Average** & **46.51\%** & **38.52\%** & **44.04\%** & **70.48\%** & **88.62\%** & **89.23\%** \\ \hline \hline \multicolumn{6}{c}{**Costs for ratings’ missclassifications**} & **1** & **2** & **3** & **4** & **5** \\ \hline
2 & 0.5 & 0 & 0.5 & 1 & 1.5 \\
3 & 1 & 0.5 & 0 & 0.5 & 1 \\
4 & 1.5 & 1 & 0.5 & 0 & 0.5 \\
5 & 2 & 1.5 & 1 & 0.5 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification accuracies of decision trees and an ensemble of decision trees on all datasets.