mlsanityData to 7 Oct 2026

The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification

arXiv 1503.01161 ↗First posted 3 Mar 2015stat.ML cs.LGcited by 76

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Abstract (arXiv)

We present the Bayesian Case Model (BCM), a general framework for Bayesian case-based reasoning (CBR) and prototype classification and clustering. BCM brings the intuitive power of CBR to a Bayesian generative framework. The BCM learns prototypes, the "quintessential" observations that best represent clusters in a dataset, by performing joint inference on cluster labels, prototypes and important features. Simultaneously, BCM pursues sparsity by learning subspaces, the sets of features that play important roles in the characterization of the prototypes. The prototype and subspace representation provides quantitative benefits in interpretability while preserving classification accuracy. Human subject experiments verify statistically significant improvements to participants' understanding when using explanations produced by BCM, compared to those given by prior art.

Key facts. Cited by 76 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Mar 2015.

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Citing papers per year: 2021: 23; 2022: 19; 2023: 15; 2024: 8; 2025: 8; 2026: 3.

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  8. 2021Towards the Unification and Robustness of Perturbation and Gradient Based Explanationscited by 47
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  10. 2021The future of human-AI collaboration: a taxonomy of design knowledge for hybrid intelligence systemscited by 39
  11. 2021DISSECT: Disentangled Simultaneous Explanations via Concept Traversalscited by 37
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38 more
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  9. 2021Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligencecited by 13
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  38. 2023Advancing Post Hoc Case Based Explanation with Feature Highlightingcited by 2

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