Article

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

Authors:

Julie ShahAuthors Info & Claims

NIPS'14: Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2

Pages 1952 - 1960

Published: 08 December 2014 Publication History

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.

References

[1]

A. Aamodt. A knowledge-intensive, integrated approach to problem solving and sustained learning. Knowledge Engineering and Image Processing Group. University of Trondheim, pages 27-85, 1991.

[2]

A. Aamodt and E. Plaza. Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI communications, 1994.

[3]

D. Baehrens, T. Schroeter, S. Harmeling, M. Kawanabe, K. Hansen, and K.R. Muller. How to explain individual classification decisions. JMLR, 2010.

[4]

I. Bichindaritz and C. Marling. Case-based reasoning in the health sciences: What's next? AI in medicine, 2006.

[5]

J. Bien, R. Tibshirani, et al. Prototype selection for interpretable classification. AOAS, 2011.

[6]

D.M. Blei, A.Y. Ng, and M.I. Jordan. Latent dirichlet allocation. JMLR, 2003.

[7]

J.S. Carroll. Analyzing decision behavior: The magician's audience. Cognitive processes in choice and decision behavior, 1980.

[8]

J. Chang, J.L. Boyd-Graber, S. Gerrish, C. Wang, and D.M. Blei. Reading tea leaves: How humans interpret topic models. In NIPS, 2009.

[9]

M.S. Cohen, J.T. Freeman, and S. Wolf. Metarecognition in time-stressed decision making: Recognizing, critiquing, and correcting. Human Factors, 1996.

[10]

T. Cover and P. Hart. Nearest neighbor pattern classification. Information Theory, 1967.

[11]

P. Cunningham, D. Doyle, and J. Loughrey. An evaluation of the usefulness of case-based explanation. In CBRRD. Springer, 2003.

[12]

G. De'ath and K.E. Fabricius. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology, 2000.

[13]

J. Eisenstein, A. Ahmed, and E. Xing. Sparse additive generative models of text. In ICML, 2011.

[14]

A. Freitas. Comprehensible classification models: a position paper. ACM SIGKDD Explorations, 2014.

[15]

S. Goh and C. Rudin. Box drawings for learning with imbalanced data. In KDD, 2014.

[16]

A. Graf, O. Bousquet, G. Rätsch, and B. Schölkopf. Prototype classification: Insights from machine learning. Neural computation, 2009.

[17]

T.L. Griffiths and M. Steyvers. Finding scientific topics. PNAS, 2004.

[18]

T. Hofmann. Probabilistic latent semantic indexing. In ACM SIGIR, 1999.

[19]

J.J. Hull. A database for handwritten text recognition research. TPAMI, 1994.

[20]

J. Huysmans, K. Dejaeger, C. Mues, J. Vanthienen, and B. Baesens. An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models. DSS, 2011.

[21]

G.A. Klein. Do decision biases explain too much. HFES, 1989.

[22]

K. Lang. Newsweeder: Learning to filter netnews. In ICML, 1995.

[23]

B. Letham, C. Rudin, T. McCormick, and D. Madigan. Interpretable classifiers using rules and Bayesian analysis. Technical report, University of Washington, 2014.

[24]

H. Li and J. Sun. Ranking-order case-based reasoning for financial distress prediction. KBSI, 2008.

[25]

J.W. Murdock, D.W. Aha, and L.A. Breslow. Assessing elaborated hypotheses: An interpretive case-based reasoning approach. In ICCBR. Springer, 2003.

[26]

A. Newell and H.A. Simon. Human problem solving. Prentice-Hall Englewood Cliffs, 1972.

[27]

X. Phan and C. Nguyen. GibbsLDA++, AC/C++ implementation of latent dirichlet allocation using gibbs sampling for parameter estimation and inference, 2013.

[28]

S. Slade. Case-based reasoning: A research paradigm. AI magazine, 1991.

[29]

F. Sørmo, J. Cassens, and A. Aamodt. Explanation in case-based reasoning-perspectives and goals. AI Review, 2005.

[30]

R. Tibshirani. Regression shrinkage and selection via the lasso. JRSS, 1996.

[31]

B. Ustun and C. Rudin. Methods and models for interpretable linear classification. ArXiv, 2014.

[32]

S. Williamson, C. Wang, K. Heller, and D. Blei. The IBP compound dirichlet process and its application to focused topic modeling. 2010.

[33]

J. Zhu, A. Ahmed, and E.P. Xing. MedLDA: maximum margin supervised topic models. JMLR, 2012.

Cited By

Kenny EDelaney EKeane MElkind E(2023)Advancing post-hoc case-based explanation with feature highlightingProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/48(427-435)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/48
Ye JWei YWen XMa CHuang ZLiu KShan HEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion RecognitionProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611704(5956-5965)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3611704
Wu MParbhoo SHughes MRoth VDoshi-Velez F(2022)Optimizing for Interpretability in Deep Neural Networks with Tree RegularizationJournal of Artificial Intelligence Research10.1613/jair.1.1255872(1-37)Online publication date: 4-Jan-2022
https://dl.acm.org/doi/10.1613/jair.1.12558
Show More Cited By

The Bayesian case model: a generative approach for case-based reasoning and prototype classification
1. Computing methodologies

Recommendations

Variational Bayesian inference for a nonlinear forward model

Variational Bayes (VB) has been proposed as a method to facilitate calculations of the posterior distributions for linear models, by providing a fast method for Bayesian inference by estimating the parameters of a factorized approximation to the ...
Variational Bayesian Monte Carlo
NIPS'18: Proceedings of the 32nd International Conference on Neural Information Processing Systems

Many probabilistic models of interest in scientific computing and machine learning have expensive, black-box likelihoods that prevent the application of standard techniques for Bayesian inference, such as MCMC, which would require access to the gradient ...
Discrete Bayesian Network Classifiers: A Survey

We have had to wait over 30 years since the naive Bayes model was first introduced in 1960 for the so-called Bayesian network classifiers to resurge. Based on Bayesian networks, these classifiers have many strengths, like model interpretability, ...

Comments

Information & Contributors

Information

Published In

cover image Guide Proceedings

NIPS'14: Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2

December 2014

3697 pages

Publisher

MIT Press

Cambridge, MA, United States

Publication History

Published: 08 December 2014

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

31
Total Citations
View Citations
4
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 28 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Kenny EDelaney EKeane MElkind E(2023)Advancing post-hoc case-based explanation with feature highlightingProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/48(427-435)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/48
Ye JWei YWen XMa CHuang ZLiu KShan HEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion RecognitionProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611704(5956-5965)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3611704
Wu MParbhoo SHughes MRoth VDoshi-Velez F(2022)Optimizing for Interpretability in Deep Neural Networks with Tree RegularizationJournal of Artificial Intelligence Research10.1613/jair.1.1255872(1-37)Online publication date: 4-Jan-2022
https://dl.acm.org/doi/10.1613/jair.1.12558
Kaur DUslu SRittichier KDurresi A(2022)Trustworthy Artificial Intelligence: A ReviewACM Computing Surveys10.1145/349120955:2(1-38)Online publication date: 18-Jan-2022
https://dl.acm.org/doi/10.1145/3491209
Liu NDu MGuo RLiu HHu X(2021)Adversarial Attacks and DefensesACM SIGKDD Explorations Newsletter10.1145/3468507.346851923:1(86-99)Online publication date: 29-May-2021
https://dl.acm.org/doi/10.1145/3468507.3468519
Qu JArguello JWang YDemartini GZuccon GCulpepper JHuang ZTong H(2021)A Study of Explainability Features to Scrutinize Faceted Filtering ResultsProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482409(1498-1507)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482409
Alqaraawi ASchuessler MWeiß PCostanza EBerthouze NPaternò FOliver NConati CSpano LTintarev N(2020)Evaluating saliency map explanations for convolutional neural networksProceedings of the 25th International Conference on Intelligent User Interfaces10.1145/3377325.3377519(275-285)Online publication date: 17-Mar-2020
https://dl.acm.org/doi/10.1145/3377325.3377519
Lai VLiu HTan CBernhaupt RMueller FVerweij DAndres JMcGrenere JCockburn AAvellino IGoguey ABjørn PZhao SSamson BKocielnik R(2020)"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for HumansProceedings of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3313831.3376873(1-13)Online publication date: 21-Apr-2020
https://dl.acm.org/doi/10.1145/3313831.3376873
Ghorbani AWexler JZou JKim BWallach HLarochelle HBeygelzimer Ad'Alché-Buc FFox E(2019)Towards automatic concept-based explanationsProceedings of the 33rd International Conference on Neural Information Processing Systems10.5555/3454287.3455119(9277-9286)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3454287.3455119
Laugel TLesot MMarsala CRenard XDetyniecki M(2019)The dangers of post-hoc interpretabilityProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367243.3367428(2801-2807)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367243.3367428
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Table of Contents