Comparing statistical and machine learning classifiers: alternatives for predictive modeling in human factors research.
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.
Carnahan
Brian
B
Department of Industrial and Systems Engineering, Auburn University, 207 Dunstan Hall, Auburn, AL 36849, USA. carnahan@eng.auburn.edu
Meyer
Gérard
G
Kuntz
Lois-Ann
LA
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001288:Attention; D001334:Automobile Driving; D015331:Cohort Studies; D003657:Decision Making; D003663:Decision Trees; D016002:Discriminant Analysis; D006804:Ergonomics / Q000379:methods*; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011237:Predictive Value of Tests; D011930:Reaction Time; D012107:Research Design; D013647:Task Performance and Analysis