Progress of PREFAB: Preference based Self-Annotation on a Low-Budget

Self-annotation is significantly important in affective computing field but has the limitation of heavily relying on human cognition. To address this, we propose a method named PREFerence-based self-Annotation on a low-Budget (PREFAB). This paper shares our progress, focusing on predicting player arousal levels from game trajectories using a RankNet-based preference learning approach. Experimental results demonstrate that our method significantly outperforms existing benchmarks in terms of training performance. In terms of practicality, it successfully captures the tendency of the changes in players' arousal better than traditional approach.