Animal Biotelemetry

Table 2 Performance comparison of four different machine-learning algorithms using the same input data set

From: Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system

Behaviour	Performance metric	Classifier algorithm
1-min window		Decision-tree	K-means	HMM	SVM
Lying	Sensitivity	74.09	85.93	90.17	92.91
	Precision	96.57	91.88	85.41	89.65
Standing	Sensitivity	82.08	59.50	38.35	51.65
	Precision	47.01	29.28	37.28	77.01
Feeding	Sensitivity	95.65	59.92	83.83	98.01
	Precision	92.03	86.13	91.54	91.01
Overall	Sensitivity	83.94	68.45	70.78	80.85
	Precision	78.53	69.09	71.41	85.89
5-min window
Lying	Sensitivity	74.09	55.37	80.31	92.91
	Precision	97.95	98.10	94.51	91.66
Standing	Sensitivity	88.46	69.23	76.92	60.89
	Precision	47.92	69.23	54.05	79.15
Feeding	Sensitivity	97.44	99.36	97.44	98.29
	Precision	93.25	87.08	93.25	92.36
Overall	Sensitivity	86.66	74.65	84.89	84.03
	Precision	79.71	84.80	80.60	87.72
10-min window
Lying	Sensitivity	77.42	80.65	70.97	89.60
	Precision	98.63	96.15	100.00	93.35
Standing	Sensitivity	88.00	76.00	92.00	68.00
	Precision	55.00	59.38	50	76.04
Feeding	Sensitivity	98.78	98.78	100	100.00
	Precision	93.10	90.00	93.18	93.18
Overall	Sensitivity	88.06	85.14	87.65	85.86
	Precision	82.24	81.84	81.06	87.52

Performance measures (sensitivity and precision) were obtained using 1-min, 5-min and 10-min windows. HMM refers to the hidden Markov model, and SVM refers to the support vector machine algorithm. Overall sensitivity is calculated as the arithmetic mean sensitivity for the three behaviours. Overall precision is calculated in a similar manner
Values marked in bold indicate the best performing algorithm for each behaviour classification

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ISSN: 2050-3385

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