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Understanding Behaviors in Different Domains: The Role of Machine Learning Techniques and Network Science

  • Grace Teo
  • Lauren Reinerman-Jones
  • Joseph McDonnell
  • Hayden J. Trainor
  • Rainier A. Porras
  • Jacob G. Feuerman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10915)

Abstract

Recent developments in the Internet of Things (IoT), social media, and the data sciences have resulted in larger volumes of data than ever before, offering more opportunity for observing and understanding behaviors. Advances in data analytic and machine learning techniques have also enabled assessments to be more multi-faceted, incorporating data from more sources. Machine learning algorithms such as Decision Trees and Random Forests, K-nearest neighbors, and Artificial Neural Networks have been used to uncover hidden patterns in data and derive predictions and recommendations from a wide range of data types and sources. However, these do not necessarily yield insights into behaviors in complex systems/domains. Methods from mathematics such as Set Theory, Graph Theory, and Network Science may be useful in shedding light on the interactions and relationships within and across domains. This paper provides a description of the applications, strengths, and limitations of some of these techniques and methods.

Keywords

Machine learning techniques Decision tree Random forest K-nearest neighbor Artificial Neural Network Network science 

Notes

Acknowledgements

This research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-15-2-0100. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied of the Army Research Laboratory of or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Grace Teo
    • 1
  • Lauren Reinerman-Jones
    • 1
  • Joseph McDonnell
    • 2
  • Hayden J. Trainor
    • 3
  • Rainier A. Porras
    • 3
  • Jacob G. Feuerman
    • 3
  1. 1.Institute for Simulation and Training, University of Central FloridaOrlandoUSA
  2. 2.Dynamic Animation SystemsFairfaxUSA
  3. 3.United States Military AcademyWest PointUSA

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