ShapDBM: Exploring Decision Boundary Maps in Shapley Space

2026-03-23

AI Summary

This work addresses the limitations of traditional decision boundary maps (DBMs) in high-dimensional settings, where reliance on dimensionality reduction in the original feature space often leads to class overlap and ambiguous visualizations. To overcome this, the study introduces Shapley values into DBM construction for the first time, mapping data into a Shapley value space before applying dimensionality reduction techniques such as t-SNE or UMAP. This approach substantially enhances class separability and structural compactness in the resulting visualizations. Empirical evaluations demonstrate that the proposed method matches or exceeds state-of-the-art alternatives across established visualization quality metrics, yielding decision boundary maps that are not only clearer but also more interpretable—thereby facilitating deeper exploration and understanding of decision-making behaviors in high-dimensional models.

Technology Category

• Machine Learning: Dimensionality Reduction/Feature Selection
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Computer Vision: Representation Learning for Vision

Application Category

• Web Mining and Content Analysis: Web data visualization
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Decision Boundary Maps (DBMs) are an effective tool for visualising machine learning classification boundaries. Yet, DBM quality strongly depends on the dimensionality reduction (DR) technique and high dimensional space used for the data points. For complex ML datasets, DR can create many mixed classes which, in turn, yield DBMs that are hard to use. We propose a new technique to compute DBMs by transforming data space into Shapley space and computing DR on it. Compared to standard DBMs computed directly from data, our maps have similar or higher quality metric values and visibly more compact, easier to explore, decision zones.

Problem

• Decision Boundary Maps
• dimensionality reduction
• Shapley space
• classification boundaries
• visualization

Innovation

• Shapley space
• Decision Boundary Maps
• dimensionality reduction
• machine learning visualization
• ShapDBM