Fetching the paper…
Reading the bibliography…
Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions.
Using neural networks for data mining, 1998
M. W. Craven and J. W. Shavlik · 1998
Earlier work this paper cites.
Random forests
L. Breiman · 2001
Earlier work this paper cites.
Using neural network rule extraction and decision tables for credit-risk evaluation
B. Baesens, R. Setiono, C. Mues, and J. Vanthienen · 2003
Earlier work this paper cites.
The Optimality of Naive Bayes
H. Zhang · 2004
Earlier work this paper cites.
Visual explanation of evidence in additive classifiers
B. Poulin, R. Eisner, D. Szafron, P. Lu, R. Greiner, D. S. Wishart, A. Fyshe, B. Pearcy, C. MacDonell, and J. Anvik · 2006
Earlier work this paper cites.
Comprehensible credit scoring models using rule extraction from support vector machines
D. Martens, B. Baesens, T. Van Gestel, and J. Vanthienen · 2007
Earlier work this paper cites.
Assessing demand for intelligibility in context-aware applications
B. Y. Lim and A. K. Dey · 2009
Earlier work this paper cites.
Opening black box data mining models using sensitivity analysis
P. Cortez and M. J. Embrechts · 2011
Earlier work this paper cites.
Dual coordinate descent methods for logistic regression and maximum entropy models
H.-F. Yu, F.-L. Huang, and C.-J. Lin · 2011
Earlier work this paper cites.
Predicting emergency department admissions
J. Boyle, M. Jessup, J. Crilly, D. Green, J. Lind, M. Wallis, P. Miller, and G. Fitzgerald · 2012
Earlier work this paper cites.
Intelligible models for classification and regression
Y. Lou, R. Caruana, and J. Gehrke · 2012
Earlier work this paper cites.
Making machine learning models interpretable
A. Vellido, J. D. Martín-Guerrero, and P. J. Lisboa · 2012
Cited alongside, same era.
Visual methods for analyzing probabilistic classification data
B. Alsallakh, A. Hanbury, H. Hauser, S. Miksch, and A. Rauber · 2014
Cited alongside, same era.
Power to the people: The role of humans in interactive machine learning
S. Amershi, M. Cakmak, W. B. Knox, and T. Kulesza · 2014
Cited alongside, same era.
Comprehensible classification models: a position paper
A. A. Freitas · 2014
Cited alongside, same era.
INFUSE: interactive feature selection for predictive modeling of high dimensional data
J. Krause, A. Perer, and E. Bertini · 2014
Cited alongside, same era.
Explaining data-driven document classifications
D. Martens and F. Provost · 2014
Cited alongside, same era.
Principles of explanatory debugging to personalize interactive machine learning
T. Kulesza, M. Burnett, W.-K. Wong, and S. Stumpf · 2015
Later among the works it cites.
Using visual analytics to interpret predictive machine learning models
J. Krause, A. Perer, and E. Bertini · 2016
Later among the works it cites.
Interacting with predictions: Visual inspection of black-box machine learning models
J. Krause, A. Perer, and K. Ng · 2016
Later among the works it cites.
The mythos of model interpretability
Z. C. Lipton · 2016
Later among the works it cites.
”why should I trust you?”: Explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
Later among the works it cites.
Explanations considered harmful? user interactions with machine learning systems
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Modeltracker: Redesigning performance analysis tools for machine learning
S. Amershi, M. Chickering, S. M. Drucker, B. Lee, P. Simard, and J. Suh · 2015
Cited alongside, same era.
A simple tool to predict admission at the time of triage
A. Cameron, K. Rodgers, A. Ireland, R. Jamdar, and G. A. McKay · 2015
Cited alongside, same era.
Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
R. Caruana, Y. Lou, J. Gehrke, P. Koch, M. Sturm, and N. Elhadad · 2015
Cited alongside, same era.
Evaluation of a hospital admission prediction model adding coded chief complaint data using neural network methodology
N. Handly, D. A. Thompson, J. Li, D. M. Chuirazzi, and A. Venkat · 2015
Cited alongside, same era.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Cited alongside, same era.
S. Stumpf, A. Bussone, and D. O’Sullivan · 2016
Later among the works it cites.
Familiarity vs trust: A comparative study of domain scientists’ trust in visual analytics and conventional analysis methods
A. Dasgupta, J.-Y. Lee, R. Wilson, R. A. Lafrance, N. Cramer, K. Cook, and S. Payne · 2017
Closest in time.
Towards better analysis of machine learning models: A visual analytics perspective
S. Liu, X. Wang, M. Liu, and J. Zhu · 2017
Closest in time.
Squares: Supporting interactive performance analysis for multiclass classifiers
D. Ren, S. Amershi, B. Lee, J. Suh, and J. D. Williams · 2017
Closest in time.
An analysis of machine-and human-analytics in classification
G. K. Tam, V. Kothari, and M. Chen · 2017
Closest in time.
Interpreting black-box classifiers using instance-level visual explanations
P. Tamagnini, J. Krause, A. Dasgupta, and E. Bertini · 2017
Closest in time.