Machine learning model explanation apparatus and methods

Arendt, Dustin; Huang, Zhuanyi

Machine learning model explanation apparatus and methods

Patent · Tue Oct 24 04:00:00 EDT 2023

OSTI ID:2293768

Arendt, Dustin; Huang, Zhuanyi

Explanation apparatus and methods are described. In one aspect, an explanation apparatus includes processing circuity configured to access a source instance which has been classified by a machine learning model; create associations of the source instance with a plurality of training instances; and process the associations of the source instance and the training instances to identify a first subset of the training instances which have less relevance to the classification decision of the source instance by the machine learning model compared with a second subset of the training instances; and an interface configured to communicate information to a user, and wherein the processing circuitry is configured to control the user interface to communicate the second subset of the training instances to the user as evidence to explain the classification of the source instance by the machine learning model.

View Patent

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE

DOE Contract Number:: AC05-76RL01830

Assignee:: Battelle Memorial Institute (Richland, WA)

Patent Number(s):: 11,797,881

Application Number:: 16/555,530

OSTI ID:: 2293768

Country of Publication:: United States

Language:: English

References (21)

Evaluation of Features for Leaf Discrimination Silva, Pedro F. B.; Marçal, André R. S.; da Silva, Rubim M. Almeida Lecture Notes in Computer Science https://doi.org/10.1007/978-3-642-39094-4_23	book	January 2013
The role of trust in automation reliance Dzindolet, Mary T.; Peterson, Scott A.; Pomranky, Regina A. International Journal of Human-Computer Studies, Vol. 58, Issue 6 https://doi.org/10.1016/S1071-5819(03)00038-7	journal	June 2003
Gene Selection for Cancer Classification using Support Vector Machines Guyon, Isabelle; Weston, Jason; Barnhill, Stephen Machine Learning, Vol. 46, Issue 1/3, p. 389-422 https://doi.org/10.1023/A:1012487302797	journal	January 2002
Mitigating belief projection in explainable artificial intelligence via Bayesian teaching Yang, Scott Cheng-Hsin; Vong, Wai Keen; Sojitra, Ravi B. Scientific Reports, Vol. 11, Issue 1 https://doi.org/10.1038/s41598-021-89267-4	journal	May 2021
Extracting insights from the shape of complex data using topology Lum, P. Y.; Singh, G.; Lehman, A. Scientific Reports, Vol. 3, Issue 1 https://doi.org/10.1038/srep01236	journal	February 2013
Barcodes: The persistent topology of data Ghrist, Robert Bulletin of the American Mathematical Society, Vol. 45, Issue 01 https://doi.org/10.1090/S0273-0979-07-01191-3	journal	October 2007
Matplotlib: A 2D Graphics Environment Hunter, John D. Computing in Science & Engineering, Vol. 9, Issue 3 https://doi.org/10.1109/MCSE.2007.55	journal	January 2007
INFUSE: Interactive Feature Selection for Predictive Modeling of High Dimensional Data Krause, Josua; Perer, Adam; Bertini, Enrico IEEE Transactions on Visualization and Computer Graphics, Vol. 20, Issue 12 https://doi.org/10.1109/TVCG.2014.2346482	journal	December 2014
Crush Your Data with ViC²ES Then CHISSL Away Arendt, Dustin L.; Franklin, Lyndsey R.; Yang, Fumeng 2018 IEEE Symposium on Visualization for Cyber Security (VizSec) https://doi.org/10.1109/VIZSEC.2018.8709212	conference	October 2018
Nuclear feature extraction for breast tumor diagnosis Street, W. N.; Wolberg, W. H.; Mangasarian, O. L. SPIE Proceedings https://doi.org/10.1117/12.148698	conference	July 1993
Approximate nearest neighbors: towards removing the curse of dimensionality Indyk, Piotr; Motwani, Rajeev Proceedings of the thirtieth annual ACM symposium on Theory of computing - STOC '98 https://doi.org/10.1145/276698.276876	conference	January 1998
Interacting with Predictions Krause, Josua; Perer, Adam; Ng, Kenney Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems https://doi.org/10.1145/2858036.2858529	conference	May 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Ribeiro, Marco Tulio; Singh, Sameer; Guestrin, Carlos Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16 https://doi.org/10.1145/2939672.2939778	conference	January 2016
Characterizing the Role of Environment on Phenotypic Traits using Topological Data Analysis Kamruzzaman, Methun; Kalyanaraman, Ananth; Krishnamoorthy, Bala Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics https://doi.org/10.1145/2975167.2985646	conference	October 2016
Interpreting Black-Box Classifiers Using Instance-Level Visual Explanations Tamagnini, Paolo; Krause, Josua; Dasgupta, Aritra Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics https://doi.org/10.1145/3077257.3077260	conference	May 2017
Intellingo Coppers, Sven; Van den Bergh, Jan; Luyten, Kris Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems https://doi.org/10.1145/3173574.3174098	conference	April 2018
The effects of example-based explanations in a machine learning interface Cai, Carrie J.; Jongejan, Jonas; Holbrook, Jess Proceedings of the 24th International Conference on Intelligent User Interfaces https://doi.org/10.1145/3301275.3302289	conference	March 2019
"What is relevant in a text document?": An interpretable machine learning approach Arras, Leila; Horn, Franziska; Montavon, Grégoire PLOS ONE, Vol. 12, Issue 8 https://doi.org/10.1371/journal.pone.0181142	journal	August 2017
UMAP: Uniform Manifold Approximation and Projection McInnes, Leland; Healy, John; Saul, Nathaniel Journal of Open Source Software, Vol. 3, Issue 29 https://doi.org/10.21105/joss.00861	journal	September 2018
Foundations for an Empirically Determined Scale of Trust in Automated Systems Jian, Jiun-Yin; Bisantz, Ann M.; Drury, Colin G. https://doi.org/10.21236/ADA388787	report	February 1998
Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR Wachter, Sandra; Mittelstadt, Brent; Russell, Chris SSRN Electronic Journal https://doi.org/10.2139/ssrn.3063289	journal	November 2017

Similar Records

How Do Visual Explanations Foster End Users' Appropriate Trust in Machine Learning?

Conference · Wed Apr 01 00:00:00 EDT 2020 · OSTI ID:1616684

CHISSL: A Human-Machine Collaboration Space for Unsupervised Learning

Conference · Fri Jul 14 00:00:00 EDT 2017 · OSTI ID:1373854

Building environment data collection systems

Patent · Mon Dec 24 23:00:00 EST 2018 · OSTI ID:1531387

Machine learning model explanation apparatus and methods

Citation Formats

References (21)

Similar Records

Related Subjects