The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
This work addresses the need for interpretable AI in domains where understanding model decisions is crucial, though it is incremental as it builds on existing Bayesian and case-based reasoning approaches.
The paper tackles the problem of making machine learning models more interpretable by introducing the Bayesian Case Model (BCM), a framework that learns prototypes and subspaces for case-based reasoning and classification, resulting in statistically significant improvements in human understanding compared to prior methods.
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.