Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification
Table 3: The robustness analysis on the FIGER dataset.
['[BOLD] Metrics', '[BOLD] Best', '[BOLD] Worst', '[BOLD] Average', '[BOLD] STDEV']
Algorithm Robustness Previous researches Morimoto and Doya Thus, we design an experiment to detect whether the trained meta-leaner is sensitive to the initialization. For each group of policies, we train a classifier with wT and evaluate it with pT using the same metric. The results demonstrate that our trained meta-learner is robust to different initialization, which indicates that the meta-learner in our method can generate high-quality and robust training and prediction policies to improve the multi-label classification.