# Boosting the Learning for Ranking Patterns
## 6 Experiments
### Interactive Learning Results  
In this section, we address the last research question **RQ4**, and conduct two experiments to evaluate the robustness and performance of our approach in the presence of human mistakes.  
Our first experiment aims to evaluate the robustness of our approach in the face of human mistakes. To simulate situations where the user feedback may be incorrect, we randomly select a set of queries and swap the user preference with a probability Err. Specifically, a user who prefers pattern \(P_{i}\) over \(P_{j}\) will mistakenly prefer \(P_{j}\) over \(P_{i}\) with a probability Err.  
Figure 5 compares the performance of RankingSVM.1 and AHPRank.1 under different levels of human error, ranging from 0% to 40%, and reports results averaged over 10 runs on the whole set of datasets using Chi-Emu and submitting 20 queries to the user. Our results show that AHPRank.1 is quite stable and robust even with high levels of human error, up to 40% (8 mistakes out of 20). The overall correlation between the learned function and the user (\(\rho\)) remains stable at 77% without mistakes and drops only to 70% under Err = 40%. However, RankingSVM.1 is stable only up to Err = 20%, and then its accuracy drops significantly, with \(\rho\) decreasing from 71% to 54%. In terms of recall, the decline is less than 2% at R@10% and 8% at R@1% under AHPRank.1, while RankingSVM.1 experiences a decline exceeding 10% at both R@10% and R@1%. In terms of CPU time, we observe waiting times ranging from 6 to 12 s under RankingSVM.1, whereas our approach never exceeds 0.02 s between two queries.  
These results suggest that our approach is robust and can handle user mistakes, while RankingSVM.1 is less robust and can suffer from significant drops in accuracy when faced with human mistakes. Additionally, our approach provides faster query processing times, which is critical in interactive data mining scenarios where human users expect near-instantaneous feedback.  
Our second experiment aims to evaluate the robustness of our approach when faced with an undecided user who begins with a set of preferences \(A\) but ends up with preferences \(B\). Here, the initial user preferences \(A\) are biased towards the presented patterns until the user is more comfortable with preferences \(B\).  
Figure 5: Learning under mistakes (20 queries).  
To simulate such situations where the user's target function changes during the learning process, we conduct an experiment where we start with Lex-Emu as the target function to learn, and after \(x\) queries, we switch to Rand-Emu. This learning process takes \((20-x)\) more queries, and we refer to it as the Lex2 Rand target. We choose to switch from Lex-Emu to Rand-Emu because the two functions are linear. We compare RankingSVM.1 and AHPRank.1 on the Lex2 Rand(x) target for a total of 20 queries, where \(x\in 0,5,10\). Note that with \(x=0\), Lex2 Rand(0) is equivalent to Rand-Emu.  
Figure 6 shows the averaged results of 10 runs on all datasets over 20 queries. The main observation that we can draw is that AHPRank.1 is stable even when faced with a changing linear function to learn, even if the user's preferences change halfway through the learning process (i.e., after 10 queries). However, changing the target function during the learning process can significantly impact the accuracy of RankingSVM.1 (a decline of 44% in terms of \(\rho\)). Regarding CPU time, our approach is 50 times faster than RankingSVM.1.