Machine learning prediction and classification of behavioral selection in a canine olfactory detection program

Abstract

There is growing interest in canine behavioral research specifically for working dogs. Here we take advantage of a dataset of a Transportation Safety Administration olfactory detection cohort of 628 Labrador Retrievers to perform Machine Learning (ML) prediction and classification studies of behavioral traits and environmental effects. Data were available for four time points over a 12 month foster period after which dogs were accepted into a training program or eliminated. Three supervised ML algorithms had robust performance in correctly predicting which dogs would be accepted into the training program, but poor performance in distinguishing those that were eliminated (~ 25% of the cohort). The 12 month testing time point yielded the best ability to distinguish accepted and eliminated dogs (AUC = 0.68). Classification studies using Principal Components Analysis and Recursive Feature Elimination using Cross-Validation revealed the importance of olfaction and possession-related traits for an airport terminal search and retrieve test, and possession, confidence, and initiative traits for an environmental test. Our findings suggest which tests, environments, behavioral traits, and time course are most important for olfactory detection dog selection. We discuss how this approach can guide further research that encompasses cognitive and emotional, and social and environmental effects.

Figure 1

(a) Radar plots of the mean scores for each of the traits for the airport terminal tests. (b) Radar plots of the mean scores for each of the traits in the environmental tests; M03 = BX (gift shop), M06 = Woodshop, M09 = Airport Cargo, M12 = Airport Terminal.

Figure 2

Principal Component Analysis (PCA) results for airport terminal (a) and environmental (b) tests. Each time point displays a heatmap displaying the relative amount of variance captured by each trait within the top 2 components.