{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T00:53:12Z","timestamp":1749775992004,"version":"3.40.5"},"reference-count":23,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,1,2]],"date-time":"2020-01-02T00:00:00Z","timestamp":1577923200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chongqing Education Committee Science and Technology Research Project","award":["KJ150057"],"award-info":[{"award-number":["KJ150057"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2020,1,2]]},"abstract":"<jats:p>Deep learning models, such as deep convolutional neural network and deep long-short term memory model, have achieved great successes in many pattern classification applications over shadow machine learning models with hand-crafted features. The main reason is the ability of deep learning models to automatically extract hierarchical features from massive data by multiple layers of neurons. However, in many other situations, existing deep learning models still cannot gain satisfying results due to the limitation of the inputs of models. The existing deep learning models only take the data instances of an input point but completely ignore the other data points in the dataset, which potentially provides critical insight for the classification of the given input. To overcome this gap, in this paper, we show that the neighboring data points besides the input data point itself can boost the deep learning model\u2019s performance significantly and design a novel deep learning model which takes both the data instances of an input point and its neighbors\u2019 classification responses as inputs. In addition, we develop an iterative algorithm which updates the neighbors of data points according to the deep representations output by the deep learning model and the parameters of the deep learning model alternately. The proposed algorithm, named \u201cIterative Deep Neighborhood (IDN),\u201d shows its advantages over the state-of-the-art deep learning models over tasks of image classification, text sentiment analysis, property price trend prediction, etc.<\/jats:p>","DOI":"10.1155\/2020\/9868017","type":"journal-article","created":{"date-parts":[[2020,1,2]],"date-time":"2020-01-02T18:30:47Z","timestamp":1577989847000},"page":"1-10","source":"Crossref","is-referenced-by-count":7,"title":["Iterative Deep Neighborhood: A Deep Learning Model Which Involves Both Input Data Points and Their Neighbors"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5445-4612","authenticated-orcid":true,"given":"Rong","family":"Liu","sequence":"first","affiliation":[{"name":"School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonggang","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9582-4966","authenticated-orcid":true,"given":"Jing-Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"New York University Abu Dhabi, Abu Dhabi, UAE"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3470-9"},{"volume":"1","year":"2016","key":"3"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-017-3241-z"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3466-5"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.3390\/s18040944"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3463-8"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.3390\/s18040952"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3468-3"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.3390\/s18030924"},{"first-page":"1","volume-title":"Learning convolutional ranking-score function by query preference regularization","year":"2017","key":"14"},{"first-page":"134","volume-title":"Cross-domain attribute representation based on convolutional neural network","year":"2018","key":"15"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1109\/72.554195"},{"first-page":"37","volume-title":"Long short-term memory","year":"2012","key":"18"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105535"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1007\/s12065-019-00227-4"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1007\/s12065-018-0171-3"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1007\/s10700-019-09311-x"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.3390\/s18041060"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1109\/34.879790"},{"key":"31","doi-asserted-by":"publisher","DOI":"10.1186\/s13640-015-0071-8"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2020\/9868017.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2020\/9868017.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2020\/9868017.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,1,2]],"date-time":"2020-01-02T18:31:00Z","timestamp":1577989860000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cin\/2020\/9868017\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,2]]},"references-count":23,"alternative-id":["9868017","9868017"],"URL":"https:\/\/doi.org\/10.1155\/2020\/9868017","relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"type":"print","value":"1687-5265"},{"type":"electronic","value":"1687-5273"}],"subject":[],"published":{"date-parts":[[2020,1,2]]}}}