Feature optimization method for white feather broiler health monitoring technology

Abstract

The existing research on white feather broiler health monitoring technology concentrate on the selection of classification algorithms and optimization of internal parameters, ignoring the white feather broiler sound signal data set (abbr. signal data set) and internal features for training classifiers. In this paper, the authors shifted the research focus to the signal data set, and proposed an applicable feature optimization method in order to improve the practicality and stability of broiler health monitoring technology. First, for outliers in the signal data set, the boxplot was used to detect them, and they were transformed into missing values. For all missing values in the signal data set, six missing value processing methods were used to process them, respectively. The optimal decision tree classifier (abbr. DT classifier) filling was determined. Second, three normalization methods were used to process the signal data set, respectively, and the optimal min–max normalization method was determined. Then, the Pearson correlation coefficient and the p-value were used to perform correlation tests on features and labels in the signal data set, then the linear–nonlinear-fusion-based (abbr. LNLF-based) feature selection method was newly proposed to select those features that have a great influence on the classification effect of the random forest classifier (abbr. RF classifier). Multiple tests had verified its superiority. Finally, ten white feather broiler sound signal validation data sets (abbr. validation data sets) were used to verify the proposed feature optimization method. It shows that the average classification accuracy achieved by the RF classifier on the signal data set before and after feature optimization improved from 72.85% to 85.28%, with an improvement of 12.43%. The average G-mean achieved by the RF classifier improved from 0.828 to 0.939, with an improvement of 0.111. The average classification accuracy and average G-mean achieved by the RF classifier on the validation data set before and after feature optimization improved significantly, with an average improvement of 13.26% and 0.093, respectively. This method improves the reliability of the existing classifiers in another way, which is an important supplement to the feature engineering study, and promotes the research progress of the existing white feather broiler health monitoring technology.