E. Alfaro, M. Gámez, and N. García, adabag: An R package for classification with boosting and bagging, J. Stat. Softw, vol.54, pp.1-35, 2013.

F. A. Alvarenga, I. Borges, L. Palkovi?, J. Rodina, V. H. Oddy et al., Using a three-axis accelerometer to identify and classify sheep behaviour at pasture, Appl. Anim. Behav. Sci, vol.181, pp.91-99, 2016.

A. L. Andriamandroso, Cattle grazing dynamics under contrasted pasture characteristics at temporal and spatial scales (Sciences agronomiques et ingénierie biologique), 2017.

Z. E. Barker, J. A. Vázquez-diosdado, E. A. Codling, N. J. Bell, H. R. Hodges et al., Use of novel sensors combining local positioning and acceleration to measure feeding behavior differences associated with lameness in dairy cattle, J. Dairy Sci, vol.101, pp.6310-6321, 2018.

J. Barwick, D. W. Lamb, R. Dobos, M. Welch, and M. Trotter, Categorising sheep activity using a tri-axial accelerometer, Comput. Electron. Agric, vol.145, pp.289-297, 2018.

S. Benaissa, F. A. Tuyttens, D. Plets, H. Cattrysse, L. Martens et al., Classification of ingestive-related cow behaviours using RumiWatch halter and neck-mounted accelerometers, Appl. Anim. Behav. Sci, 2018.

L. Riaboff, Computers and Electronics in Agriculture, vol.169, p.105179, 2020.

S. Benaissa, F. A. Tuyttens, D. Plets, T. De-pessemier, J. Trogh et al., On the use of on-cow accelerometers for the classification of behaviours in dairy barns, Res. Vet. Sci, 2017.

S. Bersch, D. Azzi, R. Khusainov, I. Achumba, and J. Ries, Sensor Data Acquisition and Processing Parameters for Human Activity Classification, Sensors, vol.14, pp.4239-4270, 2014.

L. Breiman, Random forests, Mach. Learn, vol.45, pp.5-32, 2001.

C. J. Burges, A tutorial on support vector machines for pattern recognition, Data Min. Knowl. Discov, vol.2, pp.121-167, 1998.

E. Burow, T. Rousing, P. T. Thomsen, N. D. Otten, and J. T. Sørensen, Effect of grazing on the cow welfare of dairy herds evaluated by a multidimensional welfare index, Animal, vol.7, pp.834-842, 2013.

P. C. Carvalho, Harry Stobbs memorial lecture: can grazing behavior support innovations in grassland management?, Trop. Grassl, vol.1, pp.137-155, 2013.

N. Chapinal, A. M. De-passillé, D. M. Weary, A. G. Von-keyserlingk, and J. Rushen, Using gait score, walking speed, and lying behavior to detect hoof lesions in dairy cows, J. Dairy Sci, 2009.

T. Chen, T. He, M. Benesty, V. Khotilovich, Y. Tang et al., , 2018.

J. Cohen, A coefficient of agreement for nominal scales, Educ. Psychol. Meas, vol.20, pp.37-46, 1960.

R. Dutta, D. Smith, R. Rawnsley, G. Bishop-hurley, J. Hills et al., Dynamic cattle behavioural classification using supervised ensemble classifiers, Comput. Electron. Agric, pp.18-28, 2015.

, Farm Animal Welfare Committee (FAWC) [WWW Document, 2011.

B. Fida, I. Bernabucci, D. Bibbo, S. Conforto, and M. Schmid, Pre-processing effect on the accuracy of event-based activity segmentation and classification through inertial sensors, Sensors, vol.15, pp.23095-23109, 2015.

D. Figo, P. C. Diniz, D. R. Ferreira, and J. M. Cardoso, Preprocessing techniques for context recognition from accelerometer data, Pers. Ubiquitous Comput, vol.14, pp.645-662, 2010.

G. D. Forney, The Viterbi algorithm, Proc. IEEE, vol.61, pp.268-278, 1973.

J. H. Friedman, Greedy function approximation: a gradient boosting machine, Ann. Stat, vol.29, pp.1189-1232, 2001.

W. J. Fulkerson, K. Mckean, K. S. Nandra, and I. M. Barchia, Benefits of accurately allocating feed on a daily basis to dairy cows grazing pasture, Aust. J. Exp. Agric, vol.45, p.331, 2005.

V. Giovanetti, M. Decandia, G. Molle, M. Acciaro, M. Mameli et al., Automatic classification system for grazing, ruminating and resting behaviour of dairy sheep using a tri-axial accelerometer, Comput. Electron. Agric, vol.196, pp.91-102, 2015.

W. Hamalainen, M. Jarvinen, P. Martiskainen, and J. Mononen, Jerk-based feature extraction for robust activity recognition from acceleration data, 2011 11th International Conference on Intelligent Systems Design and Applications. Presented at the 2011 11th International Conference on Intelligent Systems Design and Applications (ISDA), pp.831-836, 2011.

C. Kamphuis, B. Delarue, C. R. Burke, and J. Jago, Field evaluation of 2 collarmounted activity meters for detecting cows in estrus on a large pasture-grazed dairy farm, J. Dairy Sci, vol.95, pp.3045-3056, 2012.

A. Karatzoglou, A. Smola, K. Hornik, and A. Zeileis, kernlab -An S4 Package for Kernel Methods in R, J. Stat. Softw, vol.11, pp.1-20, 2004.

M. Kuhn, J. Wing, S. Weston, A. Williams, C. Keefer et al., , 2018.

A. Liaw and M. Wiener, Classification and Regression by randomForest, R News, vol.2, pp.18-22, 2002.

L. Lush, R. P. Wilson, M. D. Holton, P. Hopkins, K. A. Marsden et al., Classification of sheep urination events using accelerometers to aid improved measurements of livestock contributions to nitrous oxide emissions, Comput. Electron. Agric, vol.150, pp.170-177, 2018.

P. Martiskainen, M. Järvinen, J. P. Skön, J. Tiirikainen, M. Kolehmainen et al., Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines, Appl. Anim. Behav. Sci, vol.119, pp.32-38, 2009.

M. J. Mathie, A. C. Coster, N. H. Lovell, and B. G. Celler, Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement, Physiol. Meas, vol.25, pp.1-20, 2004.

D. Mcsweeney, C. Foley, P. Halton, and B. O'brien, Calibration of an automated grass measurement tool to enhance the precision of grass measurement in pasture based farming systems, Teagasc Ag Conference, 2015.

M. Norring, J. Häggman, H. Simojoki, P. Tamminen, C. Winckler et al., Short communication: Lameness impairs feeding behavior of dairy cows, J. Dairy Sci, vol.97, pp.4317-4321, 2014.

K. O'driscoll, E. Lewis, and E. Kennedy, Effect of feed allowance at pasture on the lying behaviour of dairy cows, Appl. Anim. Behav. Sci, vol.213, pp.40-46, 2019.

P. D. Penning, A technique to record automatically some aspects of grazing and ruminating behaviour in sheep, Grass Forage Sci, vol.38, pp.89-96, 1983.

. R-core-team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2019.

A. Rahman, D. V. Smith, B. Little, A. B. Ingham, P. L. Greenwood et al., Cattle behaviour classification from collar, halter, and ear tag sensors, Inf. Process. Agric, vol.5, pp.124-133, 2018.

L. Riaboff, S. Aubin, N. Bédère, S. Couvreur, A. Madouasse et al., Evaluation of pre-processing methods for the prediction of cattle behaviour from accelerometer data, Comput. Electron. Agric, vol.165, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02375859

L. Riaboff, N. Bedere, S. Couvreur, S. Aubin, E. Goumand et al., Influence of pasture characteristics and time of day on dairy cow behaviour predicted from GPS-data, Gen Meet. Eur. Grassl. Fed. EGF, vol.23, issue.7, pp.803-809, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01827948

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, Comput. Electron. Agric, vol.67, pp.80-84, 2009.

J. Rushen, N. Chapinal, and A. De-passillé, Automated monitoring of behaviouralbased animal welfare indicators, Anim. Welf, vol.21, pp.339-350, 2012.

C. J. Rutten, A. G. Velthuis, W. Steeneveld, and H. Hogeven, Sensors to support health management on dairy farms, J. Dairy Sci, pp.1928-1952, 2013.

E. Schlecht, U. Dickhoefer, E. Gumpertsberger, and A. Buerkert, Grazing itineraries and forage selection of goats in the Al Jabal al Akhdar mountain range of northern Oman, J. Arid Environ, vol.73, pp.355-363, 2009.

K. E. Schütz, A. R. Rogers, Y. A. Poulouin, N. R. Cox, and C. B. Tucker, The amount of shade influences the behavior and physiology of dairy cattle, J. Dairy Sci, vol.93, p.9, 2010.

M. S. Shahriar, D. Smith, A. Rahman, M. Freeman, J. Hills et al., Detecting heat events in dairy cows using accelerometers and unsupervised learning, Comput. Electron. Agric, vol.128, pp.20-26, 2016.

H. Shi, H. Wang, Y. Huang, L. Zhao, C. Qin et al., A hierarchical method based on weighted extreme gradient boosting in ECG heartbeat classification, Comput. Methods Programs Biomed, vol.171, pp.1-10, 2019.

D. Smith, A. Rahman, G. J. Bishop-hurley, J. Hills, S. Shahriar et al., Behavior classification of cows fitted with motion collars: Decomposing multi-class classification into a set of binary problems, Comput. Electron. Agric, vol.131, pp.40-50, 2016.

A. Subasi, D. H. Dammas, R. D. Alghamdi, R. A. Makawi, E. A. Albiety et al., Sensor based human activity recognition using adaboost ensemble classifier, Procedia Comput. Sci, vol.140, pp.104-111, 2018.

J. A. Vázquez-diosdado, Z. E. Barker, H. R. Hodges, J. R. Amory, D. P. Croft et al., Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system, Anim. Biotelemet, vol.3, 2015.

J. S. Walker, M. W. Jones, R. S. Laramee, M. D. Holton, E. L. Shepard et al., Prying into the intimate secrets of animal lives; software beyond hardware for comprehensive annotation in 'Daily Diary' tags, Mov. Ecol, 2015.

J. Werner, C. Umstatter, E. Kennedy, J. Grant, L. Leso et al., Identification of possible cow grazing behaviour indicators for restricted grass availability in a pasture-based spring calving dairy system, Livest. Sci, vol.220, pp.74-82, 2019.

J. A. Willshire and N. J. Bell, An economic review of cattle lameness, Cattle Pract, vol.17, pp.136-141, 2009.

R. Wilson, E. Shepard, and N. Liebsch, Prying into the intimate details of animal lives: use of a daily diary on animals, Endanger. Species Res, vol.4, pp.123-137, 2008.

I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2011.

J. Yang, Toward physical activity diary: motion recognition using simple acceleration features with mobile phones, Proceedings of the 1st International Workshop on Interactive Multimedia for Consumer Electronics, pp.1-10, 2009.

C. Yunta, I. Guasch, and A. Bach, Short communication: Lying behavior of lactating dairy cows is influenced by lameness especially around feeding time, J. Dairy Sci, vol.95, pp.6546-6549, 2012.

N. Zaccarelli, B. Li, I. Petrosillo, and G. Zurlini, Order and disorder in ecological time-series: Introducing normalized spectral entropy, Ecol. Indic, vol.28, pp.22-30, 2013.

L. Riaboff, Computers and Electronics in Agriculture, vol.169, p.105179, 2020.