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Machine Learning to Augment the Fusion Process for Data Classification

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Explainable AI and Other Applications of Fuzzy Techniques (NAFIPS 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 258))

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Abstract

The fusion of feature data has the ability to greatly aid the task of data classification. However, in most situations, some features are better suited to aid in the classification then others. In this research, we utilize a model-free reinforcement learning approach, coupled with Dempster-Shafer fusion calculus, to learn the subset of features to use for classification of data from multiple classes. Our approach is compared with using all features for data classification on an automobile feature data set, and the results show the benefits of our approach.

This research was funded by NAVAIR grant N68335-20-G-1004.

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Author information

Authors and Affiliations

  1. ISEA TEK, LLC, 620 N. Wymore Road, Ste. 260, Maitland, FL, USA

    Michael J. Hirsch

  2. Colorado Engineering, Inc., 1915 Jamboree Drive, Ste. 165, Colorado Springs, CO, USA

    James A. Crowder

Authors
  1. Michael J. Hirsch
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  2. James A. Crowder
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Corresponding author

Correspondence to Michael J. Hirsch .

Editor information

Editors and Affiliations

  1. Purdue University, West Lafayette, IN, USA

    Julia Rayz

  2. Purdue University, West Lafayette, IN, USA

    Victor Raskin

  3. University of Alberta, Edmonton, AB, Canada

    Scott Dick

  4. University of Texas at El Paso, El Paso, TX, USA

    Vladik Kreinovich

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Hirsch, M.J., Crowder, J.A. (2022). Machine Learning to Augment the Fusion Process for Data Classification. In: Rayz, J., Raskin, V., Dick, S., Kreinovich, V. (eds) Explainable AI and Other Applications of Fuzzy Techniques. NAFIPS 2021. Lecture Notes in Networks and Systems, vol 258. Springer, Cham. https://doi.org/10.1007/978-3-030-82099-2_14

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