The Bayesian case model: A generative approach for case-based reasoning and prototype classification

Published

Conference Paper

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 inter-pretability 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.

Duke Authors

Cited Authors

  • Kim, B; Rudin, C; Shah, J

Published Date

  • January 1, 2014

Published In

Volume / Issue

  • 3 / January

Start / End Page

  • 1952 - 1960

International Standard Serial Number (ISSN)

  • 1049-5258

Citation Source

  • Scopus