APPLICATION OF FEATURE EXTRACTION IN PIG BEHAVIOR IDENTIFICATION AND CLASSIFICATION

Abstract

ABSTRACT The aim of this study was to improve the accuracy of pig behavior identification and classification using a feature extraction method. Pig activity was measured with a triaxial accelerometer, capturing acceleration data in the X, Y, and Z directions. Statistical features, including the mean, median, maximum, minimum, first quartile, and third quartile for each axis, were extracted to form a 21-dimensional dataset. ReliefF and random forest algorithms were used to analyze and rank the significance of each feature for behavior identification and classification. Features with minimal impact were removed, reducing the dataset from 21 to 9 dimensions. The results showed that when using the ReliefF-reduced dataset, the major mean accuracy for identifying and classifying behaviors of Pigs A, B, and C was 80.9%, 81.7%, and 82.0%, respectively. Similarly, when using the random forest-reduced dataset, the major mean accuracy was 86.4%, 85.3%, and 87.2%, respectively. Thus, the random forest algorithm demonstrated superior performance in feature extraction and dimensionality reduction for classifying pig behavior in this study.

Keywords

• triaxial accelerometer
• feature extraction
• dimensionality reduction
• behavior identification and classification
• artificial neural network