Comparing Statistical and Machine Learning Classifiers: Alternatives for Predictive Modeling in Human Factors Research

Details

• Personal Author:
  Carnahan B ; Kuntz L-A ; Meyer G
• Description:
  Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches - genetic programming and decision tree induction - were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes. [Description provided by NIOSH]
• Subjects:
  Occupational Health Safety Software Transportation
• Keywords:
  Computer Models Human Factors Engineering Models Statistical Analysis Transportation Workers Truck Drivers
• ISSN:
  0018-7208
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Alabama ; Maine ; OSHA Region 1 ; OSHA Region 3 ; OSHA Region 4 ; Pennsylvania
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  408-423
• Volume:
  45
• Issue:
  3
• NIOSHTIC Number:
  nn:20057632
• Citation:
  Hum Factors 2003 Sep; 45(3):408-423
• Contact Point Address:
  Brian Carnahan, Industrial and Systems Engineering, Auburn University, 207 Dunstan Hall, Auburn, AL 36849
• Email:
  carnahan@eng.auburn.edu
• Federal Fiscal Year:
  2003
• Performing Organization:
  Deep South Center for Occupational Health and Safety, University of Alabama at Birmingham, Birmingham, AL
• Peer Reviewed:
  True
• Start Date:
  19980701
• Source Full Name:
  Human Factors
• End Date:
  20040630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:03bc5706e7e0310b4aa52045e5ca75746ad6311e27f883466bcb22d8a9ec18a228af1b5764a10c68cb6afd0f452c617640acb9f43aedac4b44a2f0be0a71a1ae
• Download URL:
  https://stacks.cdc.gov/view/cdc/214904/cdc_214904_DS1.pdf
• File Type:
  [PDF - 391.69 KB ]