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Quantifying influence of human choice on the automated detection of Drosophila behavior by a supervised machine learning algorithm
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7743940/ https://www.ncbi.nlm.nih.gov/pubmed/33326445 http://dx.doi.org/10.1371/journal.pone.0241696 |
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author | Leng, Xubo Wohl, Margot Ishii, Kenichi Nayak, Pavan Asahina, Kenta |
author_facet | Leng, Xubo Wohl, Margot Ishii, Kenichi Nayak, Pavan Asahina, Kenta |
author_sort | Leng, Xubo |
collection | PubMed |
description | 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 the confidence levels associated with both human and computer classifications. We found that a diversity of sex combinations and tracking features was important for robust performance of the automated classifiers. In particular, features concerning the relative position of flies contained useful information for training a machine-learning algorithm. These observations shed light on the importance of human influence on tracking algorithms, the selection of training images, and the quality of annotated sample images used to benchmark the performance of a classifier (the ‘ground truth’). Evaluation of these factors is necessary for researchers to accurately interpret behavioral data quantified by a machine-learning algorithm and to further improve automated classifications. |
format | Online Article Text |
id | pubmed-7743940 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-77439402020-12-31 Quantifying influence of human choice on the automated detection of Drosophila behavior by a supervised machine learning algorithm Leng, Xubo Wohl, Margot Ishii, Kenichi Nayak, Pavan Asahina, Kenta PLoS One Research Article 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 the confidence levels associated with both human and computer classifications. We found that a diversity of sex combinations and tracking features was important for robust performance of the automated classifiers. In particular, features concerning the relative position of flies contained useful information for training a machine-learning algorithm. These observations shed light on the importance of human influence on tracking algorithms, the selection of training images, and the quality of annotated sample images used to benchmark the performance of a classifier (the ‘ground truth’). Evaluation of these factors is necessary for researchers to accurately interpret behavioral data quantified by a machine-learning algorithm and to further improve automated classifications. Public Library of Science 2020-12-16 /pmc/articles/PMC7743940/ /pubmed/33326445 http://dx.doi.org/10.1371/journal.pone.0241696 Text en © 2020 Leng et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Leng, Xubo Wohl, Margot Ishii, Kenichi Nayak, Pavan Asahina, Kenta Quantifying influence of human choice on the automated detection of Drosophila behavior by a supervised machine learning algorithm |
title | Quantifying influence of human choice on the automated detection of Drosophila behavior by a supervised machine learning algorithm |
title_full | Quantifying influence of human choice on the automated detection of Drosophila behavior by a supervised machine learning algorithm |
title_fullStr | Quantifying influence of human choice on the automated detection of Drosophila behavior by a supervised machine learning algorithm |
title_full_unstemmed | Quantifying influence of human choice on the automated detection of Drosophila behavior by a supervised machine learning algorithm |
title_short | Quantifying influence of human choice on the automated detection of Drosophila behavior by a supervised machine learning algorithm |
title_sort | quantifying influence of human choice on the automated detection of drosophila behavior by a supervised machine learning algorithm |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7743940/ https://www.ncbi.nlm.nih.gov/pubmed/33326445 http://dx.doi.org/10.1371/journal.pone.0241696 |
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