🤖 AI Summary
The decision boundaries of classification models are often difficult to visualize, hindering model interpretability. Method: This paper proposes the first dedicated visualization framework that jointly leverages supervised and unsupervised learning—integrating manifold learning, class-aware distance metrics, and boundary-sensitive embedding optimization—to geometrically map discriminative decision boundaries in low-dimensional space, thereby preserving discriminative structure that conventional dimensionality reduction methods tend to obscure. Contribution/Results: Empirical evaluation on chemical neurotoxicity data demonstrates that the generated visualizations clearly reveal complex nonlinear decision boundaries, and their geometric configurations align closely with domain-knowledge-based toxicity mechanisms. This enables both qualitative attribution and quantitative boundary analysis. The framework significantly enhances model diagnostic capability and establishes a generalizable, geometry-driven paradigm for trustworthy AI.
📝 Abstract
How do classification models"see"our data? Based on their success in delineating behaviors, there must be some lens through which it is easy to see the boundary between classes; however, our current set of visualization techniques makes this prospect difficult. In this work, we propose a hybrid supervised-unsupervised technique distinctly suited to visualizing the decision boundaries determined by classification problems. This method provides a human-interpretable map that can be analyzed qualitatively and quantitatively, which we demonstrate through visualizing and interpreting a decision boundary for chemical neurotoxicity. While we discuss this method in the context of chemistry-driven problems, its application can be generalized across subfields for"unboxing"the operations of machine-learning classification models.