A comparative study of machine learning for classification of sng and pain in mice

Abstract

Mice have been an important reference for the drug test. Currently, it is only possible to collect the feedbacks from mice through non-verbal methods. To analysis the behavior of mice with machine learning, there are usually two major challenges. The first one is individual variation on facial expression or behaviors, which might require a huge amount of data set to overcome. The second one is how to obtain reliable labels, which are fundamental to train a robust machine learning model. This study aimed on the analysis of different classification architectures along with the effects of training samples and features to reduce the impact of the above two challenges.