# Boosting the Learning for Ranking Patterns
## 6 Experiments
### Active Learning Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approaches** & **Metrics** & \(\boldsymbol{p}\)**Value** & \(\boldsymbol{z}\)**-Score** & CI \\ \hline \multirow{4}{*}{RankingSVM.1} & \(\rho\) & 0.00226 & \(-2.8373\) & 99\% \\  & R@10\% & 0.12302 & \(-1.1573\) & 70\% \\  & R@1\% & 0.06301 & \(-1.5306\) & 90\% \\ \hline \multirow{4}{*}{AHPRank.1} & \(\rho\) & 0.00034 & \(-3.3973\) & 99.9\% \\  & R@10\% & 0.00205 & \(-2.8746\) & 99\% \\ \cline{1-1}  & R@1\% & 0.00139 & \(-2.9866\) & 99\% \\ \hline \hline \end{tabular} (B) **Comparing** AHPRank.1 **with** RankingSVM.1. Table 7 presents a comparison between RankingSVM.1 and AHPRank.1 on five datasets, reporting the Spearman correlation \(\rho\), recall at 10% and 1% stopping criterion \(T\) at 10, 50, and 100 queries, and the average latency time Time between two queries for RankingSVM.1 (never exceeding the latency bound of 0.1 s). The findings hold for the Rand-Emu, Lex-Emu, and Chi-Emu functions.  
\end{table} 
Table 6: Wilcoxon signed-rank test (RG vs. SBG). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **(a)** & **(b)** & **(c)** & **(d)** & **(e)** \\ \hline \multicolumn{8}{c}{**10 Queries**} \\ \hline \multirow{4}{*}{(1)} & \(\rho\) & 0.73 & 0.04 & 0.69 & 0.99 & 0.97 \\  & R@10\% & 0.61 & 0.28 & 0.38 & 0.92 & 0.91 \\  & R@1\% & 0.44 & 0.49 & 0.36 & 0.45 & 0.67 \\  & Time & 0 & 0 & 0 & 0 & 0 \\ \hline \multirow{4}{*}{(2)} & \(\rho\) & **0.78** & **0.10** & **0.93** & 0.99 & **0.99** \\  & R@10\% & **0.66** & **0.36** & **0.80** & 0.97 & 0.91 \\  & R@1\% & **0.51** & **0.50** & **0.71** & **0.81** & **0.93** \\ \hline \multicolumn{8}{c}{**50 Queries**} \\ \hline \multirow{4}{*}{(1)} & \(\rho\) & 0.94 & **0.20** & 0.71 & 0.99 & 0.98 \\  & R@10\% & 0.77 & 0.10 & 0.39 & 0.92 & 0.91 \\  & R@1\% & 0.57 & 0 & 0.36 & 0.45 & 0.67 \\  & Time & **0** & **0** & **0** & 2 & 3 \\ \hline \multirow{4}{*}{(2)} & \(\rho\) & **0.95** & 0.10 & **0.93** & 0.99 & **0.99** \\  & R@10\% & **0.83** & **0.40** & **0.72** & **0.94** & 0.91 \\  & R@1\% & **0.66** & **0.45** & 0.51 & **0.64** & **0.93** \\ \hline \multicolumn{8}{c}{**100 Queries**} \\ \hline \multirow{4}{*}{(1)} & \(\rho\) & 0.94 & **0.29** & 0.72 & 0.99 & 0.98 \\  & R@10\% & 0.75 & 0.20 & 0.41 & 0.92 & 0.91 \\  & R@1\% & 0.53 & 0 & 0.36 & 0.45 & 0.67 \\  & Time & 0 & 2 & 3 & 3 & 10 \\ \hline \multirow{4}{*}{(2)} & \(\rho\) & **0.98** & 0.10 & **0.81** & 0.99 & **0.99** \\  & R@10\% & **0.89** & **0.35** & **0.49** & **0.95** & 0.91 \\  & R@1\% & **0.78** & **0.42** & **0.37** & **0.74** & **0.93** \\ \hline \hline \end{tabular} (1): RankingSVM.1, (2): AHPRank.1, (a): Hepatitis, (b): Connect, (c): Mushroom, (d): T40, (e): Retail.  
\end{table} 
Table 7: Qualitative evaluation of RankingSVM.1 vs. AHPRank.1. For the Retail dataset, AHPRank.1 achieves high accuracy over 10 queries, which remains stable over the following 90 queries. RankingSVM.1 results are less impressive on Retail, particularly on the recall metric. However, the main difference is in the waiting time Time, where RankingSVM.1 can keep the user waiting for more than 10 s between two queries. The same observation can be made on the other datasets, where RankingSVM.1 can be hampered by overall waiting time, even with queries of size 2. In contrast, AHPRank.1 shows an instantaneous behavior, taking less than 0.1 s between two queries. This represents a limitation in the use of RankingSVM.1, especially when the learning is integrated into an interactive data mining process, where a reasonable latency time for a human user is around a few seconds [40].