Dempster Shafer distance-based multi-classifier fusion method for pig cough recognition  

作　　者：Weizheng Shen Xipeng Wang Yanling Yin Nan Ji Baisheng Dai Shengli Kou Chen Liang 

机构地区：[1]School of Electrical Engineering and Information,Northeast Agricultural University,Harbin 150030,China [2]Heilongjiang Agricultural Technology Extension Station,Harbin 150036,China

出　　处：《International Journal of Agricultural and Biological Engineering》2024年第4期245-254,共10页国际农业与生物工程学报（英文）

基　　金：supported by the Outstanding Youth Program of the Natural Science Foundation of Heilongjiang Province of China(Grant No.YQ2023C012);the project of the National Natural Science Foundation of China(Grant No.32172784,31902210);the Academic Backbone Project of Northeast Agricultural University;the National Key Research and Development Program of China(Grant No.2019YFE0125600);the University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province(Grant No.UNPYSCT-2020092);the earmarked fund for CARS-36 and CARS-35.

摘　　要：High precision pig cough recognition and low computational cost is of great importance for the realization of early warning of pig respiratory diseases.Numerous researchers have improved the recognition rate of pig cough sounds to a certain extent from feature selection and feature fusion perspectives.However,there is still a margin for the improvement in the accuracy and complexity of existing methods.Meanwhile,it is challenging to further enhance the precision of a single classifier.Therefore,this study proposed a multi-classifier fusion strategy based on Dempster Shafer distance(DS-distance)algorithm to increase the classification accuracy.Considering the engineering implementation,the machine learning with low computational complexity for fusion was chosen.First,three metrics of accuracy and diversity between classifiers were defined,including overall accuracy(OA),double fault(DF),and overall accuracy and double fault(OADF),for selecting the base classifiers.Subsequently,a two-step base classifier selection approach based on these metrics was proposed to make an optimized selection of features and classifiers.Finally,the proposed DS-distance algorithm was used to fuse the selected base classifiers to create a classification.The sound data collected in the pig barn verified the proposed algorithm.The experimental results revealed that the overall recognition accuracy of the proposed method could reach 98.76%,which was better than the existing methods.This study has achieved a high recognition accuracy through ensembled machine learning with low computational complexity.The proposed method provided an efficient way for the quick establishment of high precision pig cough recognition model in practice.

关 键 词：pig cough recognition classifier fusion classifier selection Dempster Shafer fusion distance fusion 

分 类 号：S828[农业科学—畜牧学] S23[农业科学—畜牧兽医]