Abstract
Adaptive systems typically comprise components that interrelate and interact to enable the whole system to respond and adjust to changes in the environment, operator, and task in order to regulate or maintain a level of performance or homeostasis. In so doing, they enable a degree of individualization and customization for many technological innovations such as managing the use of automation. Adaptive systems often involve some kind of feedback or closed-loop which requires a criteria for determining invoking thresholds, as well as some type of classification algorithm that models the type of changes to which the system has to adapt. This paper outlines the issues, considerations, and challenges associated with classification in adaptive systems, and reviews several algorithms that implement the feedback loop in neuro-ergonomic applications. These include logistic regression, Naïve-Bayes, artificial neural networks (ANN), and support vector machines (SVM) techniques.
The original version of this chapter was revised: Incorrect figures have been corrected. The erratum to this chapter is available at https://doi.org/10.1007/978-3-319-94223-0_50
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Change history
30 August 2018
An erratum has been published.
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Acknowledgments
This research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911 NF-14-2-0021. 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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Teo, G., Reinerman-Jones, L. (2019). Classification Algorithms in Adaptive Systems for Neuro-Ergonomic Applications. In: Cassenti, D. (eds) Advances in Human Factors in Simulation and Modeling. AHFE 2018. Advances in Intelligent Systems and Computing, vol 780. Springer, Cham. https://doi.org/10.1007/978-3-319-94223-0_39
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