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

Weizheng Shen, Xipeng Wang, Yanling Yin, Nan Ji, Baisheng Dai, Shengli Kou, Chen Liang

Abstract


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.
Key words: pig cough recognition; classifier fusion; classifier selection; Dempster Shafer fusion; distance fusion
DOI: 10.25165/j.ijabe.20241704.8027

Citation: Shen W Z, Wang X P, Yin Y L, Ji N, Dai B S, Kou S L, et al. Dempster Shafer distance-based multi-classifier fusion method for pig cough recognition. Int J Agric & Biol Eng, 2024; 17(4): 245–254.

Keywords


pig cough recognition; classifier fusion; classifier selection; Dempster Shafer fusion; distance fusion

Full Text:

PDF

References


Ji N, Yin Y L, Shen W Z, Kou S L, Dai B S, Wang G W. Pig sound analysis: A measure of welfare. Smart Agriculture, 2022; 4(2): 19–35.

Maes D, Sibila M, Pieters M, Haesebrouck F, Segalés J, de Oliveira L G. Review on the methodology to assess respiratory tract lesions in pigs and their production impact. Veterinary Research, 2023; 54(1): 8.

Racewicz P, Ludwiczak A, Skrzypczak E, Składanowska-Baryza J, Biesiada H, Nowak T, et al. Welfare health and productivity in commercial pig herds. Animals, 2021; 11(4): 1176.

Lagua E B, Mun H-S, Ampode K M B, Chem V, Kim Y-H, Yang C-J. Artificial intelligence for automatic monitoring of respiratory health conditions in smart swine farming. Animals, 2023; 13(11): 1860.

Van Hirtum V, Guarino M, Costa A, Jans P, Hhesquiere K, Aerts J M, et al. Automatic detection of chronic pig coughing from continuous registration in field situations. In: Models and analysis of vocal emissions for biomedical applications: 3rd International workshop, Firenze: Firenze University Press. 2003; pp.251–254. doi: 10.1400/39888.

Exadaktylos V, Silva M, Ferrari S, Guarino M, Taylor C J, Aerts J M, et al. Time-series analysis for online recognition and localization of sick pig (Sus scrofa) cough sounds. The Journal of the Acoustical Society of America, 2008; 124(6): 3803–3809.

Guarino M, Jans P, Costa A, Aert J M, Berckmans D. Field test of algorithm for automatic cough detection in pig house. Computers and Electronics in Agriculture, 2008; 62(1): 22–28.

Exadaktylos V, Silva M, Aerts J-M, Taylor C J, Berckmans D. Real-time recognition of sick pig cough sounds. Computers and Electronics in Agriculture, 2008; 63(2): 207–214.

Chung Y, Oh S, Lee J, Park D, Chang H-H, Kim S. Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors, 2013; 13(10): 12929–12942.

Li X, Zhao J, Gao Y, Lei M G, Liu W H, Gong Y J. Recognition of pig cough sound based on deep belief nets. Transactions of the CSAM, 2018; 49(3): 179–186. (in Chinese)

Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 2017; 60(6): 84–90.

Yin Y L, Tu D, Shen W Z, Bao J. Recognition of sick pig cough sounds based on convolutional neural network in field situations. Information Processing in Agriculture, 2021; 8(3): 369–379.

Shen M X, Wang M Y, Liu L S, Chen J, Tai M, Zhang W. Recognition method of pig cough based on deep neural network. Transactions of the CSAM, 2022; 53(5): 257–266. (in Chinese)

Shen W Z, Tu D, Yin Y L, Bao J. A new fusion feature based on convolutional neural network for pig cough recognition in field situations. Information Processing in Agriculture, 2021; 8(4): 573–580.

Ji N, Shen W, Yin Y L, Bao J, Dai B S, Hou H D, et al. Investigation of acoustic and visual features for pig cough classification. Biosystems Engineering, 2022; 219: 281–293.

Shen W Z, Ji N, Yin Y L, Dai B S, Tu D, Sun B H, et al. Fusion of acoustic and deep features for pig cough sound recognition. Computers and Electronics in Agriculture, 2022; 197: 106994.

Yin Y L, Ji N, Wang X P, Shen W Z, Dai B S, Kou S L, et al. An investigation of fusion strategies for boosting pig cough sound recognition. Computers and Electronics in Agriculture, 2023; 205: 107645.

Wang X P, Yin Y L, Dai X P, Shen W Z, Kou S L, Dai B S. Automatic detection of continuous pig cough in a complex piggery environment. Biosystems Engineering, 2024; 238: 78–88.

Sagi O, Rokach L. Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2018; 8(4): e1249.

Huang X, Zhang L P. An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery. IEEE transactions on Geoscience and Remote Sensing, 2012; 51(1): 257–272.

Li X Y, Fan P P. Study on characteristic spectrum and multiple classifier fusion with different particle size in marine sediments. IEEE Access, 2020; 8: 157151–157160.

Mishra S, Shaw K, Mishra D, Patil S, Kotecha K, Kumar S, et al. Improving the accuracy of ensemble machine learning classification models using a novel bit-fusion algorithm for healthcare AI systems. Frontiers in Public Health, 2022; 10: 858282.

Kaur T, Gandhi T K. Classifier fusion for detection of COVID-19 from CT scans. Circuits, Systems, and Signal Processing, 2022; 41: 3397–3414.

Wang X, Wu Q X, Lin X J, Zhuo Z Q, Huang L P. Pedestrian identification based on fusion of multiple features and multiple classifiers. Neurocomputing, 2016; 188: 151–159.

Huan Z, Chen X J, Lv S Y, Geng H Y. Gait recognition of acceleration sensor for smart phone based on multiple classifier fusion. Mathematical Problems in Engineering, 2019; 2019: 6471532.

Perez A, Tabia H, Declercq D, Zanotti A. Using the conflict in Dempster–Shafer evidence theory as a rejection criterion in classifier output combination for 3D human action recognition. Image and Vision Computing, 2016; 55: 149–157.

Shiue Y-R, You G-R, Su C-T, Chen H. Balancing accuracy and diversity in ensemble learning using a two-phase artificial bee colony approach. Applied Soft Computing, 2021; 105: 107212.

Ferrari S, Silva M, Guarino M, Aerts J M, Berckmans D. Cough sound analysis to identify respiratory infection in pigs. Computers and Electronics in Agriculture, 2008; 64(2): 318–325.

Ferrari S, Silva M, Guarino M, Berckmans D. Analysis of cough sounds for diagnosis of respiratory infections in intensive pig farming. Transactions of the ASABE, 2008; 51(3): 1051–1055.

Zhao X J, Shao Y, Wang D L. CASA-based robust speaker identification. IEEE Transactions on Audio, Speech, and Language Processing, 2012; 20(5): 1608–1616.

Valero X, Alias F. Gammatone cepstral coefficients: Biologically inspired features for non-speech audio classification. IEEE Transactions on Multimedia, 2012; 14(6): 1684–1689.

Fedila M, Bengherabi M, Amrouche A. Gammatone filterbank and symbiotic combination of amplitude and phase-based spectra for robust speaker verification under noisy conditions and compression artifacts. Multimedia Tools and Applications, 2018; 77: 16721–16739.

Woźniak M, Grana M, Corchado E. A survey of multiple classifier systems as hybrid systems. Information Fusion, 2014; 16: 3–17.

Dempster A P. Upper and Lower Probabilities Induced by a Multivalued Mapping. The Annals of Mathematical Statistics, 1967; 38(2): 325–339.

Shafer G. A mathematical theory of evidence. Princeton: Princeton University Press. 1976; 292p. doi: 10.1080/00401706.1978.10489628.

Zhang S X, Lin J H, Su L, Zhou Z P. pDHS-DSET: Prediction of DNase I hypersensitive sites in plant genome using DS evidence theory. Analytical Biochemistry, 2019; 564–565: 54–63.

Sandula P, Okade M. A novel video saliency estimation method in the compressed domain. Pattern Analysis and Applications, 2022; 25: 867–878.

Pan Y, Zhang L M, Wu X G, Skibniewski M J. Multi-classifier information fusion in risk analysis. Information Fusion, 2020; 60: 121–136.

Mi J H, Li Y-F, Peng W W, Huang H-Z. Reliability analysis of complex multi-state system with common cause failure based on evidential networks. Reliability Engineering & System Safety, 2018; 174: 71–81.

Jin J, Qu T N, Xu R, Wang X Y, Cichocki A. Motor imagery EEG classification based on Riemannian sparse optimization and dempster-shafer fusion of multi-time-frequency patterns. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023; 31: 58–67.

Wang B, Li H, You J W, Chen X, Yuan X H, Feng X Z. Fusing deep learning features of triplet leaf image patterns to boost soybean cultivar identification. Computers and Electronics in Agriculture, 2022; 197: 106914.




Copyright (c) 2024 International Journal of Agricultural and Biological Engineering

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

2023-2026 Copyright IJABE Editing and Publishing Office