Imbalanced classification of mental workload using a cost-sensitive majority weighted minority oversampling strategy

Title
Imbalanced classification of mental workload using a cost-sensitive majority weighted minority oversampling strategy
Authors
Keywords
Mental workload, Human–machine system, Imbalanced learning, Cost-sensitive classification, Dynamic resampling of data space, Neural network
Journal
Cognition Technology & Work
Volume 19, Issue 4, Pages 633-653
Publisher
Springer Nature
Online
2017-11-09
DOI
10.1007/s10111-017-0447-x

Ask authors/readers for more resources

Reprint

Contact the author

Find Funding. Review Successful Grants.

Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.

Explore

Find the ideal target journal for your manuscript

Explore over 38,000 international journals covering a vast array of academic fields.

Search