The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
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 represen...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Neural Information Processing Systems Foundation, Inc.
2014
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| Online Access: | http://hdl.handle.net/1721.1/91918 https://orcid.org/0000-0003-1338-8107 |
| _version_ | 1826193435521974272 |
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| author | Kim, Been Rudin, Cynthia Shah, Julie A. |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Kim, Been Rudin, Cynthia Shah, Julie A. |
| author_sort | Kim, Been |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T09:39:11Z |
| format | Article |
| id | mit-1721.1/91918 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:39:11Z |
| publishDate | 2014 |
| publisher | Neural Information Processing Systems Foundation, Inc. |
| record_format | dspace |
| spelling | mit-1721.1/919182022-09-30T15:56:22Z The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification Kim, Been Rudin, Cynthia Shah, Julie A. Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Sloan School of Management Kim, Been Rudin, Cynthia Shah, Julie A. 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. 2014-11-26T14:48:31Z 2014-11-26T14:48:31Z 2014-12 Article http://purl.org/eprint/type/ConferencePaper http://hdl.handle.net/1721.1/91918 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. https://orcid.org/0000-0003-1338-8107 en_US http://papers.nips.cc/paper/5313-the-bayesian-case-model-a-generative-approach-for-case-based-reasoning-and-prototype-classification Proceedings of the 2014 Neural Information Processing Systems Foundation Conference (NIPS 2014) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Neural Information Processing Systems Foundation, Inc. Shah |
| spellingShingle | Kim, Been Rudin, Cynthia Shah, Julie A. The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification |
| title | The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification |
| title_full | The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification |
| title_fullStr | The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification |
| title_full_unstemmed | The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification |
| title_short | The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification |
| title_sort | bayesian case model a generative approach for case based reasoning and prototype classification |
| url | http://hdl.handle.net/1721.1/91918 https://orcid.org/0000-0003-1338-8107 |
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