Conformal predictions for information fusion - A comparative study of p-value combination methods

Balasubramanian, Vineeth N and Chakraborty, S and Panchanathan, S (2015) Conformal predictions for information fusion - A comparative study of p-value combination methods. Annals of Mathematics and Artificial Intelligence, 74 (1). pp. 45-64. ISSN 1012-2443

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The increased availability of a wide range of sensing technologies over the last few decades has resulted in an equivalent increased need for reliable information fusion methods in machine learning applications. While existing theories such as the Dempster-Shafer theory and the possibility theory have been used for several years now, they do not provide guarantees of error calibration in information fusion settings. The Conformal Predictions (CP) framework is a new game-theoretic approach to reliable machine learning, which provides a methodology to obtain error calibration under classification and regression settings. In this work, we present a methodology to extend the Conformal Predictions framework to both classification and regression-based information fusion settings. This methodology is based on applying the CP framework to each data source as an independent hypothesis test, and subsequently using p-value combination methods as a test statistic for the combined hypothesis after fusion. The proposed methodology was studied in classification and regression settings within two real-world application contexts: person recognition using multiple modalities (classification), and head pose estimation using multiple image features (regression). Our experimental results showed that quantile methods of combining p-values (such as the Standard Normal Function and the Non-conformity Aggregation methods) provided the most statistically valid calibration results, and can be considered to extend the CP framework for information fusion settings.

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IITH Creators:
IITH CreatorsORCiD
Balasubramanian, Vineeth NUNSPECIFIED
Item Type: Article
Additional Information: We would like to thank the anonymous reviewers for their invaluable feedback in iden- tifying errors in the article, as well as in improving its presentation. We would also like to thank Dr Juan Arturo Nolazco, Leibny Paola Garcia and Roberto Aceves at Tecnologico de Monterrey, Mexico, for their kind support in processing the sp eech modality of the VidTIMIT and Mobio datasets and providing us with feature vectors for analysis in this work. The authors would like to thank the National Science Foundation for their support. Any opinions, findings, and conclusions or recommendations expressed in this material, however, are those of the authors and do not necessarily reflect the views of the National Science Foundation.
Uncontrolled Keywords: Conformal predictors; Face processing applications; Information fusion; Multiple hypothesis testing
Subjects: Computer science > Big Data Analytics
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 19 Nov 2014 11:09
Last Modified: 25 Apr 2018 05:40
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