Empowering Active Learning to Jointly Optimize System and User Demands
Table 5: Averaged accuracy and MAE for all strategies (including the annealed λ strategy) for Uent and Usoft.
['[EMPTY]', 'Uent Accuracy', 'Uent Accuracy', 'Uent Accuracy', 'Uent Accuracy', 'Uent MAE', 'Uent MAE', 'Uent MAE', 'Uent MAE', 'Usoft Accuracy', 'Usoft Accuracy', 'Usoft Accuracy', 'Usoft Accuracy', 'Usoft MAE', 'Usoft MAE', 'Usoft MAE', 'Usoft MAE']
Again, we can observe that random sampling performs better in early iterations, while the other sampling strategies outperform it in latter iterations. However, conducting a Wilcoxon signed-rank test with p<0.05 shows that the active learning curves only significantly differ for the static learner. Although Uent and Usoft differ to some regard, directly comparing both aggregation functions and the respective aggregated scores Extensive work with respect to both aggregation functions as well as additional aggregation strategies remains to be investigated in future work. As can be seen, using an annealed λ (tosλ) leads to the best results with respect to the user objective for Uent but fails to improve the results for Usoft.