{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T11:07:46Z","timestamp":1730200066111,"version":"3.28.0"},"reference-count":25,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1109\/bigdata47090.2019.9006281","type":"proceedings-article","created":{"date-parts":[[2020,2,25]],"date-time":"2020-02-25T01:05:34Z","timestamp":1582592734000},"page":"5804-5813","source":"Crossref","is-referenced-by-count":2,"title":["Improving k-Nearest Neighbor Pattern Recognition Models for Privacy-Preserving Data Analysis"],"prefix":"10.1109","author":[{"given":"Walisa","family":"Romsaiyud","sequence":"first","affiliation":[]},{"given":"Henning","family":"Schnoor","sequence":"additional","affiliation":[]},{"given":"Wilhelm","family":"Hasselbring","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2003.1227989"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/72.870038"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1147\/JRD.2017.2709578","article-title":"An effective algorithm for hyperparameter optimization of neural networks","volume":"61","author":"diaz","year":"2017","journal-title":"IBM Journ of Res and Dev"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2506821"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1093\/comjnl\/47.6.662"},{"key":"ref15","article-title":"Privacy Preserving Machine Learning: Threats and Solutions","author":"ai-rubaie","year":"2018","journal-title":"IEEE Security and Privacy Magazine"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1002\/0470083980"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2188286.2188326"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487629"},{"key":"ref19","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-21858-8_2","article-title":"Foundations of Feature Selection","author":"bol\u00f3n-canedo","year":"2015","journal-title":"Feature Selection for HighDimensional Data Artificial Intelligence Foundations Theory and Algorithms"},{"journal-title":"machine learning algorithm","year":"2017","author":"bonaccorso","key":"ref4"},{"journal-title":"Introduction to Machine Learning","year":"2010","author":"alpaydin","key":"ref3"},{"key":"ref6","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-30275-7_22","article-title":"Comparing Static and Dynamic Weighted Software Coupling Metrics","author":"schnoor","year":"2019","journal-title":"Int Conf on Information and Software Technologies (ICIST)"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.21917\/ijsc.2015.0133"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.32"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2551724"},{"journal-title":"Pattern Recognition and Machine Learning","year":"2006","author":"bishop","key":"ref2"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2009.2033566"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/34.824819"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2894366"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2320415"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2892062"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8622384"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2930235"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2004.31"}],"event":{"name":"2019 IEEE International Conference on Big Data (Big Data)","start":{"date-parts":[[2019,12,9]]},"location":"Los Angeles, CA, USA","end":{"date-parts":[[2019,12,12]]}},"container-title":["2019 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8986695\/9005444\/09006281.pdf?arnumber=9006281","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,17]],"date-time":"2022-07-17T17:53:07Z","timestamp":1658080387000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9006281\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":25,"URL":"https:\/\/doi.org\/10.1109\/bigdata47090.2019.9006281","relation":{},"subject":[],"published":{"date-parts":[[2019,12]]}}}