Quantifying influence of human choice on the automated detection of Drosophila behavior by a supervised machine learning algorithm.

ABSTRACT: Automated quantification of behavior is increasingly prevalent in neuroscience research. Human judgments can influence machine-learning-based behavior classification at multiple steps in the process, for both supervised and unsupervised approaches. Such steps include the design of the algorithm for machine learning, the methods used for animal tracking, the choice of training images, and the benchmarking of classification outcomes. However, how these design choices contribute to the interpretation of automated behavioral classifications has not been extensively characterized. Here, we quantify the effects of experimenter choices on the outputs of automated classifiers of Drosophila social behaviors. Drosophila behaviors contain a considerable degree of variability, which was reflected in t

SUBMITTER: Leng X 

PROVIDER: S-EPMC7743940 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature