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Therefore, the proposed algorithm is applied in training of Multi-Layer Feed-Forward Neural Network (FFNN) for classification task in medical data mining. The classification task is carried out on 15 well-known medical data sets. A comparative study has been made with Levenberg\u2013Marquardt algorithm, Artificial Bee Colony, Trigonometric Mutation Differential Evolution, basic PSO and other nine opposition-based PSO algorithms. The experimental results with statistical analysis demonstrate that the proposed algorithm performs better than all other algorithms in classification. <\/jats:p>","DOI":"10.1142\/s0219622019500329","type":"journal-article","created":{"date-parts":[[2019,8,26]],"date-time":"2019-08-26T07:57:38Z","timestamp":1566806258000},"page":"1717-1750","source":"Crossref","is-referenced-by-count":32,"title":["Partial Opposition-Based Particle Swarm Optimizer in Artificial Neural Network Training for Medical Data Classification"],"prefix":"10.1142","volume":"18","author":[{"given":"Tapas","family":"Si","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Bankura Unnayani Institute of Engineering, Bankura, West Bengal, India"}]},{"given":"Ramkinkar","family":"Dutta","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bankura Unnayani Institute of Engineering, Bankura, West Bengal, India"}]}],"member":"219","published-online":{"date-parts":[[2019,11,8]]},"reference":[{"key":"S0219622019500329BIB001","volume-title":"Neural Networks and Learning Machines","author":"Haykin S.","year":"2011","edition":"3"},{"key":"S0219622019500329BIB002","doi-asserted-by":"publisher","DOI":"10.2478\/v10136-012-0031-x"},{"key":"S0219622019500329BIB003","doi-asserted-by":"publisher","DOI":"10.1109\/72.572107"},{"key":"S0219622019500329BIB004","doi-asserted-by":"publisher","DOI":"10.1109\/5.784219"},{"key":"S0219622019500329BIB005","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1995.488968"},{"key":"S0219622019500329BIB006","doi-asserted-by":"publisher","DOI":"10.1109\/ICEC.1998.699146"},{"key":"S0219622019500329BIB007","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2002.1007808"},{"key":"S0219622019500329BIB008","first-page":"110","volume-title":"Proc. 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