Modeling and Clustering the Behavior of Animals Using Hidden Markov Models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F16%3A00306336" target="_blank" >RIV/68407700:21240/16:00306336 - isvavai.cz</a>
Result on the web
<a href="http://ceur-ws.org/Vol-1649/172.pdf" target="_blank" >http://ceur-ws.org/Vol-1649/172.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Modeling and Clustering the Behavior of Animals Using Hidden Markov Models
Original language description
The objectives of this article are to model behavior of individual animals and to cluster the resulting models in order to group animals with similar behavior patterns. Hidden Markov models are considered suitable for clustering purposes. Their clustering is well studied, however, only if the observable variables can be assumed to be Gaussian mixtures, which is not valid in our case. Therefore, we use the Kullback-Leibler divergence to cluster hidden Markov models with observable variables that have an arbitrary distribution. Hierarchical and spectral clustering is applied. To evaluate the modeling approach, an experiment was performed and an accuracy of 83.86% was reached in predicting behavioral sequences of individual animals. Results of clustering were evaluated by means of statistical descriptors of the animals and by a domain expert, both methods confirm that the results of clustering are meaningful.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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