Context-based Unsupervised Data Fusion for Decision Making

Erfan Soltanmohammadi, Mort Naraghi-Pour, Mihaela Schaar
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2076-2084, 2015.

Abstract

Big Data received from sources such as social media, in-stream monitoring systems, networks, and markets is often mined for discovering patterns, detecting anomalies, and making decisions or predictions. In distributed learning and real-time processing of Big Data, ensemble-based systems in which a fusion center (FC) is used to combine the local decisions of several classifiers, have shown to be superior to single expert systems. However, optimal design of the FC requires knowledge of the accuracy of the individual classifiers which, in many cases, is not available. Moreover, in many applications supervised training of the FC is not feasible since the true labels of the data set are not available. In this paper, we propose an unsupervised joint estimation-detection scheme to estimate the accuracies of the local classifiers as functions of data context and to fuse the local decisions of the classifiers. Numerical results show the dramatic improvement of the proposed method as compared with the state of the art approaches.

Cite this Paper

BibTeX
@InProceedings{pmlr-v37-soltanmohammadi15, title = {Context-based Unsupervised Data Fusion for Decision Making}, author = {Soltanmohammadi, Erfan and Naraghi-Pour, Mort and Schaar, Mihaela}, booktitle = {Proceedings of the 32nd International Conference on Machine Learning}, pages = {2076--2084}, year = {2015}, editor = {Bach, Francis and Blei, David}, volume = {37}, series = {Proceedings of Machine Learning Research}, address = {Lille, France}, month = {07--09 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v37/soltanmohammadi15.pdf}, url = {https://proceedings.mlr.press/v37/soltanmohammadi15.html}, abstract = {Big Data received from sources such as social media, in-stream monitoring systems, networks, and markets is often mined for discovering patterns, detecting anomalies, and making decisions or predictions. In distributed learning and real-time processing of Big Data, ensemble-based systems in which a fusion center (FC) is used to combine the local decisions of several classifiers, have shown to be superior to single expert systems. However, optimal design of the FC requires knowledge of the accuracy of the individual classifiers which, in many cases, is not available. Moreover, in many applications supervised training of the FC is not feasible since the true labels of the data set are not available. In this paper, we propose an unsupervised joint estimation-detection scheme to estimate the accuracies of the local classifiers as functions of data context and to fuse the local decisions of the classifiers. Numerical results show the dramatic improvement of the proposed method as compared with the state of the art approaches.} }
Endnote
%0 Conference Paper %T Context-based Unsupervised Data Fusion for Decision Making %A Erfan Soltanmohammadi %A Mort Naraghi-Pour %A Mihaela Schaar %B Proceedings of the 32nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2015 %E Francis Bach %E David Blei %F pmlr-v37-soltanmohammadi15 %I PMLR %P 2076--2084 %U https://proceedings.mlr.press/v37/soltanmohammadi15.html %V 37 %X Big Data received from sources such as social media, in-stream monitoring systems, networks, and markets is often mined for discovering patterns, detecting anomalies, and making decisions or predictions. In distributed learning and real-time processing of Big Data, ensemble-based systems in which a fusion center (FC) is used to combine the local decisions of several classifiers, have shown to be superior to single expert systems. However, optimal design of the FC requires knowledge of the accuracy of the individual classifiers which, in many cases, is not available. Moreover, in many applications supervised training of the FC is not feasible since the true labels of the data set are not available. In this paper, we propose an unsupervised joint estimation-detection scheme to estimate the accuracies of the local classifiers as functions of data context and to fuse the local decisions of the classifiers. Numerical results show the dramatic improvement of the proposed method as compared with the state of the art approaches.
RIS
TY - CPAPER TI - Context-based Unsupervised Data Fusion for Decision Making AU - Erfan Soltanmohammadi AU - Mort Naraghi-Pour AU - Mihaela Schaar BT - Proceedings of the 32nd International Conference on Machine Learning DA - 2015/06/01 ED - Francis Bach ED - David Blei ID - pmlr-v37-soltanmohammadi15 PB - PMLR DP - Proceedings of Machine Learning Research VL - 37 SP - 2076 EP - 2084 L1 - http://proceedings.mlr.press/v37/soltanmohammadi15.pdf UR - https://proceedings.mlr.press/v37/soltanmohammadi15.html AB - Big Data received from sources such as social media, in-stream monitoring systems, networks, and markets is often mined for discovering patterns, detecting anomalies, and making decisions or predictions. In distributed learning and real-time processing of Big Data, ensemble-based systems in which a fusion center (FC) is used to combine the local decisions of several classifiers, have shown to be superior to single expert systems. However, optimal design of the FC requires knowledge of the accuracy of the individual classifiers which, in many cases, is not available. Moreover, in many applications supervised training of the FC is not feasible since the true labels of the data set are not available. In this paper, we propose an unsupervised joint estimation-detection scheme to estimate the accuracies of the local classifiers as functions of data context and to fuse the local decisions of the classifiers. Numerical results show the dramatic improvement of the proposed method as compared with the state of the art approaches. ER -
APA
Soltanmohammadi, E., Naraghi-Pour, M. & Schaar, M.. (2015). Context-based Unsupervised Data Fusion for Decision Making. Proceedings of the 32nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 37:2076-2084 Available from https://proceedings.mlr.press/v37/soltanmohammadi15.html.

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