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Anomaly Detection in Pet Behavioural Data

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Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2134))

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Abstract

Pet owners are increasingly becoming conscious of their pet’s necessities and are paying more attention to their overall wellness. The well-being of their pets is intricately linked to their own emotional and physical well-being. Some veterinary system solutions are emerging to provide proactive healthcare options for pets. One such solution offers the continuous monitoring of a pet’s activity through accelerometer tracking devices. Based on data collected by this application, in this paper, we study different time aggregation and three unsupervised machine learning techniques to identify anomalies in pet behaviour data. Specifically, three algorithms, Isolation Forest, Local Outlier Factor, and K-Nearest Neighbour, with various thresholds to differentiate between normal and abnormal events. Results conducted on ten pets (five cats and five dogs) show that the most effective approach is to use daily data divided into periods. Moreover, the Local Outlier Factor is the best algorithm for detecting anomalies when prioritizing the identification of true positives. However, it also produces a high false positive ratio.

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Authors and Affiliations

  1. Faculty of Economics, University of Porto, 4200-464, Porto, Portugal

    Inês Silva & João Gama

  2. Faculty of Sciences, University of Porto, 4169-007, Porto, Portugal

    Rita P. Ribeiro

  3. INESC TEC, 4200-465, Porto, Portugal

    Rita P. Ribeiro & João Gama

Authors
  1. Inês Silva
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  2. Rita P. Ribeiro
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  3. João Gama
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Corresponding author

Correspondence to Inês Silva .

Editor information

Editors and Affiliations

  1. University of Turin, Turin, Italy

    Rosa Meo

  2. Sapienza University of Rome, Rome, Italy

    Fabrizio Silvestri

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Silva, I., Ribeiro, R.P., Gama, J. (2025). Anomaly Detection in Pet Behavioural Data. In: Meo, R., Silvestri, F. (eds) Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2023. Communications in Computer and Information Science, vol 2134. Springer, Cham. https://doi.org/10.1007/978-3-031-74627-7_10

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  • DOI: https://doi.org/10.1007/978-3-031-74627-7_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-74626-0

  • Online ISBN: 978-3-031-74627-7

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