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Title: Using Machine Learning to Aid in Data Classification: Classifying Occupation Compatibility with Highly Automated Vehicles
Data classification is central to human factors research, and manual data classification is tedious and error prone. Supervised learning enables analysts to train an algorithm by manually classifying a few cases and then have that algorithm classify many cases. However, algorithms often fail to leverage human insight. To address this, we augment supervised learning with unsupervised learning and data visualization. Unsupervised learning highlights potential classification errors, explains the underlying classification, and identifies additional cases that merit manual classification. We illustrate this using the Occupational Information Network database to classify occupations as having tasks that might be performed in an automated vehicle.
Authors:
;
Award ID(s):
1839484
Publication Date:
NSF-PAR ID:
10317864
Journal Name:
Ergonomics in Design: The Quarterly of Human Factors Applications
Volume:
29
Issue:
2
ISSN:
1064-8046
Sponsoring Org:
National Science Foundation
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