The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
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Shah_The Bayesian Case.pdf
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1.42 MB
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Author(s)
Kim, Been
Rudin, Cynthia
Shah, Julie A.
Date Issued
December 2014
Journal
Proceedings of the 2014 Neural Information Processing Systems Foundation Conference (NIPS 2014)
Publisher
Neural Information Processing Systems Foundation, Inc.
Citation
Kim, Been, Cynthia Rudin, and Julie Shah. "The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification." Advances in Neural Information Processing Systems 27 (NIPS 2014), pp.1-9.
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Sloan School of Management
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
http://papers.nips.cc/paper/5313-the-bayesian-case-model-a-generative-approach-for-case-based-reasoning-and-prototype-classification
