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

Hao-En Yen, Chao-Cheng Wu, Cheng-Han Lee, Chih-Cheng Chen, Hsiao-Chi Li
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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.
机器学习对小鼠鸣声和疼痛分类的比较研究
小鼠已成为药物试验的重要参考。目前,只能通过非语言的方法来收集老鼠的反馈。用机器学习来分析老鼠的行为,通常有两个主要的挑战。第一个是面部表情或行为的个体差异,这可能需要大量的数据集来克服。第二个问题是如何获得可靠的标签,这是训练一个健壮的机器学习模型的基础。本研究旨在分析不同的分类架构以及训练样本和特征的影响,以减少上述两种挑战的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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