## 3 Results and Discussion
### Results Corresponding to All the Datasets  
Table 3 shows a selection of the results, for a subset of the pairs (Classifier:Feature Set). Given the large number of pairs, it is not possible to include all the methods in a single table (Tables A1-A4 (in the Appendix A) show the complete set of results for the RF, SVM-L, and SVM-G classifiers and the Stacking strategy, respectively, considering all the datasets). The pairs in the subset were selected so that for all data sets there was some method with the highest accuracy. In some datasets, many methods share the best accuracy, so it is not possible to include all of them in the subset. Therefore, the subset of pairs was further reduced according to the average accuracy across all the datasets. Moreover, the pairs with the feature set OB were also included because it was used by [PERSON] et al. [18]. \begin{table}
\begin{tabular}{l c} \hline
**Features** & **Abbreviation** & **N\({}^{o}\) of Attributes** \\ \hline Raw Original Data & (**O**) & 512 \\ Basic aggregation features & (**B**) & 149 \\ ROCKET with 100 kernels & (**R100**) & 200 \\ ROCKET with 1000 kernels & (**R1000**) & 2000 \\ TSFRESH without feature selection & (**T**) & 6352 \\ TSFRESH with feature selection & (**t**) & Variable (885/3567/3235) \\ Original \& Basic & (**OB**) & 661 \\ Original \& ROCKET (100) & (**OR100**) & 712 \\ Original \& ROCKET (1000) & (**OR1000**) & 2512 \\ Original \& TSFRESH (without feat. sel.) & (**OT**) & 6864 \\ Original \& TSFRESH (with feat. sel.) & (**Ot**) & Variable (1397/4079/3747) \\ Original, basic, ROCKET (1000) and TSFRESH (with feat. sel.) & (**All**) & Variable (3546/6228/5896) \\ \hline \end{tabular}
\end{table} 
Table 2: Combination of features used, including their total number. In the case of variable-size feature extraction methods, the minimum, maximum, and average values, respectively, are also given (in parenthesis). \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline  & **RF** & **RF** & **SVM-L** & **SVM-L** & **SVM-L** & **SVM-L** & **SVM-L** & **SVM-L** & **SVM-G** & **SVM-G** & **SVM-G** & **Stack** & **Stack+** \\  & **OB** & **:** & **:** & **:** & **:** & **:** & **:** & **:** & **:** & **:** & **:** & **:** & **:** & **:** \\ \hline Count + Order Lego & 96.09 & 97.09 & 98.00 & 96.00 & **99.00** & 98.00 & 96.00 & 97.00 & 98.00 & 98.00 & 98.00 & 98.00 & 98.00 \\ Count 20 chips NO & 63.60 & 66.95 & 71.88 & 72.35 & 62.49 & 73.78 & 71.87 & 73.94 & 73.62 & 71.23 & 73.94 & 77.60 & 76.17 \\ case \(\times\)30 sorted & & & & & & & & & & & & & \\ Count 20 chips WITH & 70.32 & 77.14 & 78.89 & 79.68 & 79.52 & 81.27 & 80.00 & 80.63 & 80.63 & 79.05 & 83.33 & 80.95 & 80.79 \\ case \(\times\)30 sorted & & & & & & & & & & & & & \\ Count 20 supers \(\times\)10 & 75.71 & 75.24 & **91.43** & 84.29 & 87.14 & 89.52 & 84.76 & 86.19 & 90.00 & 87.62 & 88.10 & 83.33 & 83.81 \\ Distance 3 mugs 10 distances & 47.22 & 64.44 & 80.56 & **88.00** & 47.56 & 75.11 & 72.22 & 77.33 & 80.56 & 50.78 & 62.56 & 64.78 & 63.67 \\ Distance 3 mugs grouped & 84.33 & **100.00** & 93.44 & **100.00** & 80.89 & 90.33 & **100.00** & **100.00** & **100.00** & 92.44 & 80.89 & 94.67 & 99.00 & **100.00** \\ Distance 7 slotting & **100.00** & **100.00** & **100.00** & 98.33 & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** \\ Flip 10 endreaders NO & 83.64 & 86.36 & 84.09 & 88.18 & 82.73 & 85.91 & 89.55 & 88.64 & 85.45 & 88.18 & 85.00 & 87.73 & 86.82 \\ case & & & & & & & & & & & & & \\ Flip 10 endreaders & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** \\ WITH case & & & & & & & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 3: Results for a subset of the classifiers with different feature sets. Table 4 presents the average accuracy of each one of the pairs (Classifier:Feature Set) assessed throughout the 34 datasets.