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

TL;DR

The Bayesian Case Model (BCM) introduces a probabilistic framework that learns representative prototypes and feature subspaces for clusters, enhancing interpretability and accuracy in case-based reasoning and classification.

Contribution

BCM is the first to integrate Bayesian inference with prototype learning and subspace selection for improved interpretability in clustering and classification.

Findings

BCM achieves comparable or better accuracy than existing methods.

Human studies show BCM explanations improve understanding.

BCM provides interpretable prototypes with feature importance.

Abstract

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.