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The existing waste classification models driven by deep learning are not easy to achieve accurate results and still need to be improved due to the various architecture networks adopted. Their performance on different datasets is varied, and there is also a lack of specific large\u2010scale datasets for training. We propose a new combination classification model based on three pretrained CNN models (VGG19, DenseNet169, and NASNetLarge) for processing the ImageNet database and achieve high classification accuracy. In our proposed model, the transfer learning model based on each pretrained model is constructed as a candidate classifier, and the optimal output of three candidate classifiers is selected as the final classification result. The experiments based on two waste image datasets demonstrate that the proposed model achieves 96.5% and 94% classification accuracy and outperforms several counterpart methods.<\/jats:p>","DOI":"10.1002\/cpe.5751","type":"journal-article","created":{"date-parts":[[2020,4,14]],"date-time":"2020-04-14T22:58:26Z","timestamp":1586905106000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":69,"title":["A combination model based on transfer learning for waste classification"],"prefix":"10.1002","volume":"32","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-8698-2946","authenticated-orcid":false,"given":"Guang\u2010Li","family":"Huang","sequence":"first","affiliation":[{"name":"School of Science RMIT University Melbourne Victoria Australia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6488-1052","authenticated-orcid":false,"given":"Jing","family":"He","sequence":"additional","affiliation":[{"name":"School of Software and Electrical Engineering Swinburne University of Technology Melbourne Victoria Australia"}]},{"given":"Zenglin","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1821-8644","authenticated-orcid":false,"given":"Guangyan","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Information Technology Deakin University Melbourne Victoria Australia"}]}],"member":"311","published-online":{"date-parts":[[2020,4,14]]},"reference":[{"key":"e_1_2_7_2_1","volume-title":"Urban Development Series Knowledge Papers","author":"Hoornweg D","year":"2012"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph16183349"},{"key":"e_1_2_7_4_1","unstructured":"YangM ThungG. 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