🤖 AI Summary
Evaluating and comparing multi-label classifiers in high-dimensional label spaces remains challenging due to the lack of intuitive, scalable visualization methods. To address this, we propose an interactive visual analytics framework that operates without reliance on confusion matrices. The framework jointly models predictions from three complementary perspectives—instances, labels, and classifiers—and integrates label-level performance aggregation, coordinated multi-view navigation, and scalable rendering for comparative analysis across multiple classifiers. Its key innovation lies in decoupling visualization design from the number of labels, thereby enabling real-time exploration even with hundreds of labels. A user study demonstrates that our approach significantly improves both assessment efficiency and analytical insight depth. It achieves high scalability and strong interpretability while preserving operational simplicity—making it particularly suitable for diagnosing classifier behavior in large-scale multi-label settings.
📝 Abstract
Machine learning-based classifiers are commonly evaluated by metrics like accuracy, but deeper analysis is required to understand their strengths and weaknesses. MLMC is a visual exploration tool that tackles the challenge of multi-label classifier comparison and evaluation. It offers a scalable alternative to confusion matrices which are commonly used for such tasks, but don't scale well with a large number of classes or labels. Additionally, MLMC allows users to view classifier performance from an instance perspective, a label perspective, and a classifier perspective. Our user study shows that the techniques implemented by MLMC allow for a powerful multi-label classifier evaluation while preserving user friendliness.