Bag of Class Posteriors, a new multivariate time series classifier applied to animal behaviour identification. Issue 7 (1st May 2015)

• Record Type: Journal Article
• Authors: Smith, Daniel; Dutta, Ritaban; Hellicar, Andrew; Bishop-Hurley, Greg; Rawnsley, Richard; Henry, David; Hills, James; Timms, Greg
• Is Part Of: Expert systems with applications. Volume 42:Issue 7(2015)
• Page Start: 3774
• Page End: 3784
• Publication Date: 2015-05-01
• Subjects: Time series classification -- Class posterior estimates -- Precision cattle management -- Inertial Measurement Units; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2014.11.033
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A new multi-scale time series classifier is proposed using class posterior estimates. The classifier infers a large set of animal behaviour using motion based time series. The proposed classifier outperforms benchmark classifiers by between 43% and 77%. The proposed classifier is found to be more efficient than the Bag of Features model. Abstract: In this paper, two new multivariate time series classifiers are introduced as the Bag of Class Posteriors (BOCP) and the Bag of Class Posterior with Ordering (BOCPO). The models propose a new multi-scale feature representation where the class posterior estimates of contiguous local patterns are aggregated over longer time scales. The models are employed as part of an animal behaviour monitoring system that are comprised of sensors, which are fitted to the animals, and a classifier that translates sensor data into knowledge of the animal's behaviour. Animal monitoring systems are commonly developed to infer a small number of behaviours with relevance to a specific application. To investigate if a standard monitoring system with an Inertial Measurement Unit (IMU) can be reused for different management applications, a set of ten cattle behaviours relevant to different management applications were classified with the proposed models. Results indicate that the multi-scale BOCP and BOCPO models were far more capable of classifying the cow behaviours offering a 43% to 77% improvement over benchmark time interval classifiers with fixed time resolution. In addition, the BOCPO model was shown to offer a far more efficient feature representation than the related multi-scale Bag of Features (BOF) classifier (up to 200 times smaller) making it better suited to deploy upon monitoring devices fitted to animals in the field.