Structure-Based Classification Approach

Abstract

This study introduces a novel structure-based classification (SBC) framework that leverages pairwise distance representations of rating data to enhance classification performance while mitigating individual differences in scale usage. Unlike conventional feature-based approaches that rely on absolute rating scores, SBC transforms rating data into structured representations by computing pairwise distances between rating dimensions. This transformation captures the relational structure of ratings, ensuring consistency between training and test datasets and enhancing model robustness. To evaluate the effectiveness of this approach, we conducted a simulation study in which participants rated stimuli across multiple affective dimensions, with systematic individual differences in scale usage. The results demonstrated that SBC successfully classified affective stimuli despite these variations, performing comparably to traditional classification methods. The findings suggest that relational structures among rating dimensions contain meaningful information for affective classification, akin to functional connectivity approaches in cognitive neuroscience. By focusing on rating interdependencies as well as absolute values, SBC provides a robust and generalizable method for analyzing subjective responses, with implications for psychological research.