Abstract
To address the problem of the low accuracy and poor robustness of modeling methods for imbalanced data sets of pig behavior identification and classification, the three commonly used re-sampling methods of under-sampling, SMOTE and Borderline-SMOTE are compared, and an adaptive boundary data augmentation algorithm AD-BL-SMOTE is proposed. The activity of the pigs was measured using triaxial accelerometers, which were fixed on the backs of the pigs. A multilayer feed-forward neural network was trained and validated with 21 input features to classify four pig activities: lying, standing, walking, and exploring. The results showed that re-sampling methods are an effective way to improve the performance of pig behavior identification and classification. Moreover, AD-BL-SMOTE could yield greater improvements in classification performance than the other three methods for balancing the training data set. The overall major mean accuracy of lying, standing, walking, and exploring by pigs A, B and C was significantly improved by using AD-BL-SMOTE, reaching 91.8%, 93.0% and 96.0%, respectively.
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