An explainable statistical learning algorithm to support data fusion

Kenneth Dayman; Jason Hite; Adam Drescher; Brian Ade

doi:10.23919/FUSION45008.2020.9190238

An explainable statistical learning algorithm to support data fusion

Kenneth Dayman, Jason Hite, Adam Drescher, Brian Ade

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper presents a statistical learning algorithm called the relevance vector machine that is currently under development to support data fusion applications. The algorithm is applicable to classification and regression problems and has been shown to be capable of learning complex, explainable behaviors in real engineering problems. This article summarizes construction of the learning algorithm and provides an example application to demonstrate some of the capabilities of the relevance vector machine with feature fusion. Finally, the possibilities are presented for using the relevance vector machine to support multi-modal data fusion by exploiting the statistically consistent outputs given by the model to extend binary label fusion to continuous label fusion.

Original language	English
Title of host publication	Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9780578647098
DOIs	https://doi.org/10.23919/FUSION45008.2020.9190238
State	Published - Jul 2020
Event	23rd International Conference on Information Fusion, FUSION 2020 - Virtual, Pretoria, South Africa Duration: Jul 6 2020 → Jul 9 2020

Publication series

Name	Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020

Conference

Conference	23rd International Conference on Information Fusion, FUSION 2020
Country/Territory	South Africa
City	Virtual, Pretoria
Period	07/6/20 → 07/9/20

Funding

This work was funded by the Office of Defense Nuclear Nonproliferation Research and Development (NA-22), within the US Department of Energy’s National Nuclear Security Administration. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Funders	Funder number
US Department of Energy
U.S. Department of Energy
National Nuclear Security Administration	DE-AC05-00OR22725
Office of Defense Nuclear Nonproliferation	NA-22

Keywords

Bayesian inference
Label fusion
Machine learning

Access to Document

10.23919/FUSION45008.2020.9190238

Cite this

Dayman, K., Hite, J., Drescher, A., & Ade, B. (2020). An explainable statistical learning algorithm to support data fusion. In Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020 Article 9190238 (Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/FUSION45008.2020.9190238

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title = "An explainable statistical learning algorithm to support data fusion",

abstract = "This paper presents a statistical learning algorithm called the relevance vector machine that is currently under development to support data fusion applications. The algorithm is applicable to classification and regression problems and has been shown to be capable of learning complex, explainable behaviors in real engineering problems. This article summarizes construction of the learning algorithm and provides an example application to demonstrate some of the capabilities of the relevance vector machine with feature fusion. Finally, the possibilities are presented for using the relevance vector machine to support multi-modal data fusion by exploiting the statistically consistent outputs given by the model to extend binary label fusion to continuous label fusion.",

keywords = "Bayesian inference, Label fusion, Machine learning",

author = "Kenneth Dayman and Jason Hite and Adam Drescher and Brian Ade",

note = "Publisher Copyright: {\textcopyright} 2020 International Society of Information Fusion (ISIF).; 23rd International Conference on Information Fusion, FUSION 2020 ; Conference date: 06-07-2020 Through 09-07-2020",

year = "2020",

month = jul,

doi = "10.23919/FUSION45008.2020.9190238",

language = "English",

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Dayman, K, Hite, J, Drescher, A & Ade, B 2020, An explainable statistical learning algorithm to support data fusion. in Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020., 9190238, Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020, Institute of Electrical and Electronics Engineers Inc., 23rd International Conference on Information Fusion, FUSION 2020, Virtual, Pretoria, South Africa, 07/6/20. https://doi.org/10.23919/FUSION45008.2020.9190238

An explainable statistical learning algorithm to support data fusion. / Dayman, Kenneth; Hite, Jason; Drescher, Adam et al.
Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9190238 (Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - This paper presents a statistical learning algorithm called the relevance vector machine that is currently under development to support data fusion applications. The algorithm is applicable to classification and regression problems and has been shown to be capable of learning complex, explainable behaviors in real engineering problems. This article summarizes construction of the learning algorithm and provides an example application to demonstrate some of the capabilities of the relevance vector machine with feature fusion. Finally, the possibilities are presented for using the relevance vector machine to support multi-modal data fusion by exploiting the statistically consistent outputs given by the model to extend binary label fusion to continuous label fusion.

AB - This paper presents a statistical learning algorithm called the relevance vector machine that is currently under development to support data fusion applications. The algorithm is applicable to classification and regression problems and has been shown to be capable of learning complex, explainable behaviors in real engineering problems. This article summarizes construction of the learning algorithm and provides an example application to demonstrate some of the capabilities of the relevance vector machine with feature fusion. Finally, the possibilities are presented for using the relevance vector machine to support multi-modal data fusion by exploiting the statistically consistent outputs given by the model to extend binary label fusion to continuous label fusion.

KW - Bayesian inference

KW - Label fusion

KW - Machine learning

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M3 - Conference contribution

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Dayman K, Hite J, Drescher A, Ade B. An explainable statistical learning algorithm to support data fusion. In Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9190238. (Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020). doi: 10.23919/FUSION45008.2020.9190238

An explainable statistical learning algorithm to support data fusion

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