{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T05:27:05Z","timestamp":1773984425732,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T00:00:00Z","timestamp":1660694400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["31902209"],"award-info":[{"award-number":["31902209"]}]},{"name":"National Natural Science Foundation of China","award":["HBCT2018150208"],"award-info":[{"award-number":["HBCT2018150208"]}]},{"name":"Hebei Province, the second phase of modern agricultural industry technology system innovation team construction project","award":["31902209"],"award-info":[{"award-number":["31902209"]}]},{"name":"Hebei Province, the second phase of modern agricultural industry technology system innovation team construction project","award":["HBCT2018150208"],"award-info":[{"award-number":["HBCT2018150208"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In order to reduce the influence of redundant features on the performance of the model in the process of accelerometer behavior recognition, and to improve the recognition accuracy of the model, this paper proposes an improved Whale Optimization algorithm with mixed strategy (IWOA) combined with the extreme gradient boosting algorithm (XGBoost) as a preferred method for chicken behavior identification features. A nine-axis inertial sensor was used to obtain the chicken behavior data. After noise reduction, the sliding window was used to extract 44 dimensional features in the time domain and frequency domain. To improve the search ability of the Whale Optimization algorithm for optimal solutions, the introduction of the good point set improves population diversity and expands the search range; the introduction of adaptive weight balances the search ability of the optimal solution in the early and late stages; the introduction of dimension-by-dimension lens imaging learning based on the adaptive weight factor perturbs the optimal solution and enhances the ability to jump out of the local optimal solution. This method\u2019s effectiveness was verified by recognizing cage breeders\u2019 feeding and drinking behaviors. The results show that the number of feature dimensions is reduced by 72.73%. At the same time, the behavior recognition accuracy is increased by 2.41% compared with the original behavior feature dataset, which is 95.58%. Compared with other dimensionality reduction methods, the IWOA\u2013XGBoost model proposed in this paper has the highest recognition accuracy. The dimension reduction results have a certain degree of universality for different classification algorithms. This provides a method for behavior recognition based on acceleration sensor data.<\/jats:p>","DOI":"10.3390\/s22166147","type":"journal-article","created":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T22:53:30Z","timestamp":1660776810000},"page":"6147","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Feature Selection Model Based on IWOA for Behavior Identification of Chicken"],"prefix":"10.3390","volume":"22","author":[{"given":"Lihua","family":"Li","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China"},{"name":"Key Laboratory of Broiler Layer Breeding Facilities Engineering, Ministry of Agriculture and Rural Affairs, Baoding 071000, China"},{"name":"Hebei Provincial Key Laboratory of Livestock and Poultry Breeding Intelligent Equipment and New Energy Utilization, Baoding 071000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengzui","family":"Di","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2344-7441","authenticated-orcid":false,"given":"Hao","family":"Xue","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zixuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziqi","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,17]]},"reference":[{"key":"ref_1","first-page":"104","article-title":"Recognition of the movement behavior of stud rams based on acceleration sensor","volume":"23","author":"Zhang","year":"2018","journal-title":"J. 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