{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T20:02:07Z","timestamp":1766088127893,"version":"3.41.2"},"reference-count":37,"publisher":"World Scientific Pub Co Pte Ltd","issue":"07","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62227805"],"award-info":[{"award-number":["62227805"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Tianjin for Distinguished Young Scholars","award":["19JCJQJC61500"],"award-info":[{"award-number":["19JCJQJC61500"]}]},{"name":"Open Foundation of State Key Laboratory of Complex Electronic System Simulation, Beijing, China","award":["614201001032104"],"award-info":[{"award-number":["614201001032104"]}]},{"name":"Design and Research of Automatic Feeding Machine for Silkworm Farming","award":["2020XJYB006"],"award-info":[{"award-number":["2020XJYB006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2024,5,15]]},"abstract":"<jats:p> In this paper, we propose a cascade AdaBoost neural network (CANN) based on concepts and construct of AdaBoost neurons and cascade structure. Compared with AdaBoost, CANN can represent complex relationships between features. In CANN, representation learning is performed through AdaBoost, and the method of random selection features is utilized to encourage the diversity of AdaBoost neurons. Through the cascade structure, CANN has the context structure for complex feature representation. At the same time, in order to avoid the problem of feature disappearance, shortcut connection is used to add the previous information to the later nodes. Furthermore, particle swarm optimization (PSO) algorithm is utilized to optimize the structure of CANN, it can obtain the number of iterations to achieve better performance. Two types of CANN are proposed based \u2014 binary-classification CANN (BCANN) or multi-classification CANN (MCANN). The performance of CANN is evaluated with two kinds of data sets: machine learning data sets and atrial fibrillation data set. A comparative analysis illustrates that the proposed CANN leads to better performance than the models reported in the literature. <\/jats:p>","DOI":"10.1142\/s021812662450124x","type":"journal-article","created":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T03:22:10Z","timestamp":1696562530000},"source":"Crossref","is-referenced-by-count":3,"title":["Cascade AdaBoost Neural Network Classifier: Analysis and Design"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8042-6040","authenticated-orcid":false,"given":"Mingjie","family":"Gao","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, P. R. 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