Bag of Class Posteriors, a new multivariate time series classifier applied to animal behaviour identification

Expert Systems with Applications

Volumn 42, Issue 7, 2015, Pages 3774-3784

  Smith, Daniel ^a   Dutta, Ritaban ^a   Hellicar, Andrew ^a   Bishop Hurley, Greg ^a   Rawnsley, Richard ^b   Henry, David ^a   Hills, James ^b   Timms, Greg ^a  

^a CSIRO   (Australia)

^b UNIVERSITY OF TASMANIA   (Australia)

Author keywords

Class posterior estimates; Inertial Measurement Units; Precision cattle management; Time series classification

Indexed keywords

ANIMALS; MONITORING; TIME SERIES; UNITS OF MEASUREMENT;

CLASS POSTERIOR ESTIMATES; FEATURE REPRESENTATION; INERTIAL MEASUREMENT UNIT; MANAGEMENT APPLICATIONS; MONITORING SYSTEM; MULTI-SCALE FEATURES; MULTIVARIATE TIME SERIES; TIME SERIES CLASSIFICATIONS;

CLASSIFICATION (OF INFORMATION);

EID: 84921452225     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2014.11.033     Document Type: Article

References (49)

1. Allen, J. D., Anderson, S. D., Collier, R. J., & Smith, J. F. (2013). Managing heat stress and its impact on cow behavior. In Proc. of western dairy management conference (pp. 150-162). Reno, Nevada.
2. Bagnall, A., Davis, L., Hills, J., & Lines, J. (2012). Transformation based ensembles for time series classification. In Proc. of the twelfth SIAM international conference on data mining (pp. 308-318). Anaheim, California.
3. Barla, A., Odone, F., & Verri, A. (2003). Histogram intersection kernel for image classification. In Proc. of the IEEE international conference on image processing, Vol. 3 (pp. 513-516). Barcelona, Spain.
4. Bishop-Hurley, G., Henry, D., Smith, D., Dutta, R., Hills, J., & Rawnsley, R. et al. (2014). An investigation of cow feeding behavior using motion sensors. In Proc. of the IEEE international conference on instrumentation and measurement technology (I2MTC) (pp. 1285-1290). Montevideo, Uruguay.
5. L. Breiman Bagging predictors Machine Learning 24 2 1996 123 140
6. Briggs, F., Raich, R., & Fern, X. Z. (2009). Audio classification of bird species: A statistical manifold approach. In Proc. of 9th IEEE international conference on data mining (ICDM) (pp. 51-60). Miami, Florida.
7. S. Chen, W. Wang, and H. van Zuylen Construct support vector machine ensemble to detect traffic incident Expert Systems with Applications 36 8 2009 10976 10986
8. C. Cornoua, and S. Lundbye-Christensen Classifying sows activity types from acceleration patterns: An application of the multi-process Kalman filter Applied Animal Behaviour Science 111 2008 262 273
9. H. Deng, G.C. Runger, E. Tuv, and V. Martyanov A time series forest for classification and feature extraction Information Sciences 239 2013 142 153
10. K. De Bock, and D. Van den Poel An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction Expert Systems with Applications 38 10 2011 12293 12301
11. R. Das, I. Turkoglu, and A. Sengur Effective diagnosis of heart disease through neural networks ensembles Expert Systems with Applications 36 4 2009 7675 7680
12. K. Ellis, E. Coviello, A.B. Chan, and G. Lanckriet A bag of systems representation for music auto-tagging IEEE Transactions on Audio, Speech and Language Processing 21 12 2013 2554 2569
13. R. Fergus, P. Perona, and A. Zisserman Object class recognition by unsupervised scale-invariant learning Proc. of IEEE Conference on Computer Vision and Pattern Recognition 2 2002 264 271
14. Freund, Y., & Schapire, R. E. (1996). Experiments with a new boosting algorithm. In Proc. of the 13th international conference on machine learning (ICML) (pp. 148-156). San Francisco, California.
15. Z. Fu, G. Lu, K.M. Ting, and D. Zhang Music classification via the bag-of-features approach Pattern Recognition Letters 32 14 2011 1768 1777
16. S. Gallon, F. Bailleul, J-B. Charrassin, C. Guinet, C-A. Bost, and Y. Handrich Identifying foraging events in deep diving southern elephant seals, Mirounga leonina, using acceleration data loggers Deep Sea Research Part II, Spec Issue: Fourth International Symposium on Bio-logging Science, Hobart, Australia 2013 14 22
17. L.A. Gonzalez, K.S. Schwartzkopf-Genswein, N. Cauklett, E. Jansen, T.A. McAllister, and A.L. Scaefer Pain mitigation following band castration of beef calves and its effects on performance, behaviour, E. coli and salivary cortisol Journal of Animal Science 88 2010 802 810
18. Y. Guo, G. Poulton, P. Corke, G. Bishop-Hurley, T. Wark, and D. Swain Using accelerometer, high sample rate GPS and magnetometer data to develop a cattle movement and behaviour model Ecological Modelling 220 17 2009 2068 2075
19. A. Hyvrinen, and E. Oja Independent component analysis: Algorithms and applications Neural Networks 13 4-5 2000 411 430
20. Kuksa, P., & Pavlovic, V. (2010). Spatial representation for efficient sequence classification. In Proc. of the 20th international conference on pattern recognition (pp. 3320-3323). Instanbul, Turkey.
21. J. Lin, R. Khade, and Y. Li Rotation-invariant similarity in time series using bag-of-patterns representation Journal of Intelligent Information System 39 4-5 2012 287 315
22. J. Lin, and Y. Li Finding structural similarity in time series data using bag-of-patterns representation Proc. of IEEE international conference on scientific and statistical database management LNCS 5566 2009 Springer-Verlag New Orleans, United States 461 477
23. Liu, L., Wang, L., & Liu, X. (2011). In defense of soft-assignment coding. In Proc. of IEEE international conference on computer vision (pp. 2486-2493). Barcelona, Spain.
24. P. Martiskainen, M. Jarvinen, J-K. Skon, J. Tiirikainen, M. Kolehmainen, and J. Mononen Cow behaviour pattern recognition using a three-dimensional accerometers and support vector machines Applied Animal Behaviour Science 119 1-2 2009 32 38
25. R. Muller, and L. Schrader A new method to measure behavioural activity levels in dairy cows Applied Animal Behaviour Science 83 2003 247 258
26. J.C. Niebles, H. Wang, and L. Fei-Fei Unsupervised learning of human action categories using spatialoral words International Journal of Computer Vision 79 3 2008 299318
27. L.R. Nielsen, A.R. Pedersen, M.S. Herskin, and L. Munksgaard Quantifying walking and standing behaviour of dairy cows using a moving average based on output from an accelerometer Applied Animal Behaviour Science 127 1 2010 12 19
28. Ordnez, P., Armstrong, T., Oates, T., & Fackler, J. (2011). Using modified multivariate bag-of-words models to classify physiological data. In Proc. of 11th IEEE international conference on data mining workshops (pp. 534-539). Vancouver, Canada.
29. J. Proakis, and D. Manolakis Digital signal processing: Principles, applications and algorithms 4th ed. 2007 Pearson Education
30. B. Robert, B.J. White, D.G. Renter, and R.L. Larson Evaluation of three-dimensional accelerometers to monitor and classify behavior patterns in cattle Computers and Electronics in Agriculture 67 1-2 2009 80 84
31. Rodriguez, J. J., & Kuncheva, L. I. (2007). Time series classification: Decision forests and SVM on interval and DTW features. In Proc. of the 13th international conference on knowledge discovery and data mining. San Jose, California.
32. Select Sires, SelectDetect, accessed at http://www.selectsires.com/products/heatdetect/SelectDetect.html, (23/07/2014).
33. Semex USA, Semex ai24, accessed at http://www.semexusa.com/i?lang=en&page=ai24.shtml, (23/07/2014).
34. Seyerlehner, K., Widmer, G., & Knees, P. (2008). Frame level audio similarity - A codebook approach. In Proc. of 11th international conference on digital audio effects. Espoo, Finland.
35. Sundaram, S., & Bellegarda, J. R. (2012). Latent perceptual mapping with data-driven variable-length acoustic units for template-based speech recognition. In Proc. of IEEE international conference of acoustics, speech and signal processing (pp. 4125-4128). Kyoto, Japan.
36. E. Ungar, Z. Henkin, M. Gutma, A. Dolev, A. Genizi, and D. Ganskopp Inference of animal activity from GPS collar data on free-ranging cattle Rangeland Ecological Management 58 2005 256 266
37. J.C. van Gemert, J-M. Geusebroek, C.J. Veenman, and A.W. Smeulders Kernel codebooks for scene categorization Proc. of European conference on computer vision LNCS III 2008 Springer-Verlag 696 709
38. C.J. Van Rijsbergen Information retrieval 2nd ed. 1979 Butterworth London
39. Vidal, R., & Favaro, P. (2007). DynamicBoost: Boosting time series generated by dynamical systems. In Proc. IEEE 11th international conference on computer vision. Rio De Janeiro, Brazil.
40. M. Viviant, A.W. Trites, D.A.S. Rosen, P. Monestiez, and C. Guinet Prey capture attempts can be detected in Steller sea lions and other marine predators using accelerometers Polar Biology 33 2010 713 719
41. M.A.G. von Keyserling, J. Rushen, A.M. de Passille, and D.M. Weary Behaviour of lame cows Veterinarni Medicina 56 12 2011 581 588
42. J. Wang, P. Liu, M.H.F. Shea, S. Nahavandia, and A. Kouzani Bag-of-words representation for biomedical time series classification Biomedical Signal Processing and Control 8 6 2013 634 644
43. T. Wark, P. Corke, P. Sikka, L. Klingbeil, Y. Guo, and C. Crossman Transforming agriculture through pervasive wireless sensor networks IEEE Pervasive Computing 6 2007 50 67
44. S. Watanabe, M. Izawa, A. Kato, Y. Ropert-Coudert, and Y. Naito A new technique for monitoring the detailed behaviour of terrestrial animals: A case study with the domestic cat Applied Animal Behaviour Science 94 2005 117 131
45. D.M. Weary, J.M. Huzzey, and M.A.G. von Keyserlingk Board-invited review: Using behavior to predict and identify ill health in animals Journal of Animal Science 89 2009 770 777
46. Heuser, T. (2014). WhatISee App, accessed at https://itunes.apple.com/au/app/whatisee/id332512569?mt=8, (October 2014).
47. Ye, L., & Keogh, E. (2009). Time series shapelets: A new primitive for data mining. In Proc. of 15th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 947-956). Paris, France.
48. L. Ye, and E. Keogh Time series shapelets: A novel technique that allows accurate, interpretable and fast classification Data Mining and Knowledge Discovery 22 2011 149 182
49. Q. Zhang, S.A. Goldman, W. Yu, and J.E. Fritts Content-based image retrieval using multiple-Instance Learning Proc. of 19th international conference on machine learning 2002 Morgan Kaufmann Sydney, Australia 682 689